Pipeline run
c2572c5b-9053-41b1-b0ec-8b73981437bd
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
Take ownership of architecture design and development of scalable and distributed software systems. Translate business to technical requirements Own technical execution, ensuring code quality, adheren…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
AI Engineer
CASE Dslug: ai-engineer · id: 13 · source: db
The primary skills indicate a strong emphasis on AI technologies and cloud-based solutions.
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
About the job Below are details of open role for AI Engineer. Experience: 3+ years Budget: 1.2-1.3 LPM Location: Onsite (Noida, Pune, Bangalore) We are seeking an AI Engineer with 3+ experience in LLM/GenAI/Agentic solution development,. The ideal candidate will have strong experience working with Python, LLM solution patterns and tools (RAG, Vector DB, Agentic workflows, etc.) cloud platforms (AWS preferred, Databricks and Azure works as well), and DevOps tools. They will be responsible for designing and developing scalable LLM/Agentic solutions, architecture design, and ensuring the performance and reliability of our systems. What They Will Do Take ownership of architecture design and development of scalable and distributed software systems. Translate business to technical requirements Own technical execution, ensuring code quality, adherence to deadlines, and efficient resource allocation Data driven decision making skills with focus on achieving product goals Design, develop and deploy LLM based pipelines involving patterns like RAG, Agentic workflows, Responsible for the complete software development lifecycle, including requirements analysis, design, coding, testing, and deployment. Utilize AWS services/ Azure services like IAM, Monitoring, Load Balancing, Autoscaling, Database, Networking, storage, ECR, AKS, ACR etc. Utilize Databricks capabilities, esp. Unity Catalog and be able to architect the governance layer from data all-the-way to the AI model output Design, develop and deploy prompt and response guardrails to enable responsible AI requirements Implement DevOps practices using tools like Docker, Kubernetes to ensure continuous integration and delivery. Develop DevOps scripts for automation and monitoring. Collaborate with cross-functional teams, conduct code reviews, and provide guidance on software design and best practices. What They Will Bring Bachelor’s degree in computer science, Information Technology, or a related field (or equivalent work experience). Strong coding skills with proficiency in Python Experience with API frameworks both stateless and stateful such as Fast API, Django Proficient in cloud platforms, specifically AWS, Databricks and Azure Proficient in Infra-as-Code, esp. focused on developing Terraform scripts and Cloudformation Knowledge and hands-on experience with front-end development (React JS, Next JS, Tailwind CSS) preferred Strong experience in LLM patterns like RAG, Vector DB, Hybrid Search, Agent development, Agentic workflows, prompt engineering, etc. Strong experience with LLM APIs (Open AI, Anthropic, AWS Bedrock), SDKs (Langchain, DSPy) Hands-on experience with DevOps tools including Docker, Kubernetes, and AWS services (Redshift, RDS, S3). Hands-on experience with Databricks solutions including Unity Catalog. Experience in production deployments involving thousands of users, esp with capabilities like Redis, Vector Search, etc. Strong understanding of scalable application design principles and experience with security best practices and compliance with privacy regulations. Good knowledge of software engineering practices like version control (GIT), DevOps (Azure DevOps preferred) and Agile or Scrum. Strong communication skills, with the ability to effectively convey complex technical concepts to a diverse audience. Experience of SDLC and best practices while development Experience with Agile methodology for continuous product development and delivery Skills: azure,devops,code,api,design
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 416
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Engineer
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cybersecurity Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
FastAPI appears in many Python backend job postings and is a common choice in modern API stacks; GitHub usage and ecosystem activity remain strong, with no vendor sunset or replacement trend.
Sebastián Ramírez ·mit ·since 2018 (0.95)
FastAPI is a specific Python web framework; typical JDs won’t confuse it with other catalog frameworks.
Not versioned
Framework ·web_framework confidence 0.98
FastAPI is a framework because developers build applications inside it and it provides the application structure and request handling rather than being a standalone library or tool.
- Category
- Framework
- Sub-category
- web_framework
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Python Web API Frameworks
Pipeline tentative id
Frameworks used to build HTTP APIs and backend services in Python. FastAPI belongs here because it is a Python-first framework for defining routes, request/response models, validation, and API documentation.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Django (CANONICAL) primary
- Django 1 (VERSION)
- Django 1.x (VERSION)
- Django 2 (VERSION)
- Django 2.x (VERSION)
- Django 3 (VERSION)
- Django 3.x (VERSION)
- Django 4 (VERSION)
- Django 4.x (VERSION)
- Django 5 (VERSION)
- Django 5.x (VERSION)
- Django1 (VERSION)
- Django2 (VERSION)
- Django3 (VERSION)
- Django4 (VERSION)
- Django5 (VERSION)
- django 2 (VERSION)
- django 2.x (VERSION)
- django 3 (VERSION)
- django 3.x (VERSION)
- django 4 (VERSION)
- django 4.x (VERSION)
- django 5 (VERSION)
- django 5.0 (VERSION)
- django 5.x (VERSION)
- django2 (VERSION)
- django2.x (VERSION)
- django3 (VERSION)
- django3.x (VERSION)
- django4 (VERSION)
- django4.x (VERSION)
- django5 (VERSION)
- django5.0 (VERSION)
- django5.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Django Software Foundation
- License
- bsd
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 5
Maturity reasoning: Django appears in many backend web job descriptions and remains a standard Python web framework; its GitHub ecosystem and long-term LTS releases show sustained market demand.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Amazon
- License
- other_open
- Year introduced
- 2006
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 46
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
-
Cloud Provider Platforms Catalog dimension db id 131
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Cloud Security Posture Tools Catalog dimension db id 64
Library dimension (catalog)
Roles linked in library: Cybersecurity Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Azure (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Azure is broadly adopted and frequently appears in cloud/platform job descriptions alongside AWS and GCP; Microsoft’s ongoing enterprise investment and Azure certification demand signal strong hiring-pipeline relevance.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 46
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
-
Cloud Provider Platforms Catalog dimension db id 131
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Cloud Security Posture Tools Catalog dimension db id 64
Library dimension (catalog)
Roles linked in library: Cybersecurity Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Databricks appears frequently in data engineering and analytics job postings, especially alongside Spark, Delta Lake, and lakehouse stacks; strong vendor adoption and broad enterprise usage signal mainstream demand.
Databricks, Inc. ·other_open ·since 2013 (0.95)
“Databricks” is a specific vendor/platform name; unlikely to be confused with other distinct skills in typical JDs.
Not versioned
Platform ·data_analytics_platform confidence 0.97
By the Platform vs Tool rule, Databricks is a hosted multi-tenant environment with APIs and managed services rather than software you run yourself.
- Category
- Platform
- Sub-category
- data_analytics_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Lakehouse Data Platform
Pipeline tentative id
Databricks is a unified lakehouse platform for data engineering, analytics, and machine learning workloads. It belongs here because the skill refers to operating and building on the Databricks environment rather than a single language or algorithm.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Appears increasingly in Databricks and data-governance job postings, but JD volume is still far below core platforms like AWS or PostgreSQL; adoption is growing with Unity Catalog positioned as Databricks’ unified governance layer.
Databricks ·unknown ·since 2021 (0.85)
“Unity Catalog” is a specific data governance/lineage platform name; unlikely to be confused with other catalog-like skills in typical JDs.
Not versioned
Platform ·data_governance_platform confidence 0.90
By the Platform vs Tool rule, Unity Catalog is a hosted, multi-tenant managed governance layer with APIs rather than software you run yourself, so it fits Platform.
- Category
- Platform
- Sub-category
- data_governance_platform
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Data Lineage and Metadata Catalog dimension db id 28
Library dimension (catalog)
Roles linked in library: Data Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Data Lineage and Metadata Catalog dimension db id 28
Library dimension (catalog)
Roles linked in library: Data Engineer
Locked dimensions (v3 placement)
-
Data Lineage and Metadata
Reuses catalog slug
Cataloging, documenting, and tracing how data assets are organized, governed, and discovered across systems. Unity Catalog belongs here because it is a metadata and governance layer for tables, views, files, permissions, and lineage in the Databricks ecosystem.
-
Databricks Data Governance
Pipeline tentative id
Governance features specific to Databricks for controlling access, organizing data assets, and managing cross-workspace metadata. This is a reasonable fit when Unity Catalog is used as the primary governance plane rather than just generic metadata tooling.
-
Data Lineage and Metadata
Reuses catalog slug
Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Terraform (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Infrastructure As Code Tool
- Vendor
- HashiCorp
- License
- mpl
- Year introduced
- 2014
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Terraform is broadly listed in DevOps/SRE/cloud JDs and remains a standard IaC tool across AWS/Azure/GCP; HashiCorp’s ecosystem and widespread GitHub usage signal strong market adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 191
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Infrastructure as Code for ML Catalog dimension db id 57
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- CloudFormation (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Infrastructure As Code Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2013
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AWS CloudFormation appears in many cloud/IaC job descriptions and remains a standard AWS-native infrastructure-as-code option, alongside Terraform in hiring pipelines.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 181
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- React (CANONICAL) primary
- React 0.13 (VERSION)
- React 0.14 (VERSION)
- React 15 (VERSION)
- React 15.x (VERSION)
- React 16 (VERSION)
- React 16.x (VERSION)
- React 17 (VERSION)
- React 17.x (VERSION)
- React 18 (VERSION)
- React 18.x (VERSION)
- React 19 (VERSION)
- React v15 (VERSION)
- React v16 (VERSION)
- React v17 (VERSION)
- React v18 (VERSION)
- React v19 (VERSION)
- ReactJS 18 (VERSION)
- react 15 (VERSION)
- react 16 (VERSION)
- react 17 (VERSION)
- react 18 (VERSION)
- react 19 (VERSION)
- react15 (VERSION)
- react16 (VERSION)
- react17 (VERSION)
- react18 (VERSION)
- react19 (VERSION)
- reactjs 18 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Frontend Framework
- Vendor
- Meta
- License
- mit
- Year introduced
- 2013
- Confidence
- 0.98
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 18
Maturity reasoning: React appears in high-volume frontend job postings across startups and enterprises and remains a default hiring-pipeline skill, with strong GitHub/npm usage and ecosystem activity.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 341
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
UI Frameworks and Rendering Catalog dimension db id 115
Library dimension (catalog)
Roles linked in library: Frontend Engineer, Hybrid Mobile Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
UI Frameworks and Rendering
ui-frameworks-and-rendering
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Next.js (CANONICAL) primary
- Next 10 (VERSION)
- Next 11 (VERSION)
- Next 12 (VERSION)
- Next 13 (VERSION)
- Next 14 (VERSION)
- Next 15 (VERSION)
- Next 2 (VERSION)
- Next 3 (VERSION)
- Next 4 (VERSION)
- Next 5 (VERSION)
- Next 6 (VERSION)
- Next 7 (VERSION)
- Next 8 (VERSION)
- Next 9 (VERSION)
- Next.js 1 (VERSION)
- Next.js 10 (VERSION)
- Next.js 11 (VERSION)
- Next.js 12 (VERSION)
- Next.js 13 (VERSION)
- Next.js 14 (VERSION)
- Next.js 15 (VERSION)
- Next.js 2 (VERSION)
- Next.js 3 (VERSION)
- Next.js 4 (VERSION)
- Next.js 5 (VERSION)
- Next.js 6 (VERSION)
- Next.js 7 (VERSION)
- Next.js 8 (VERSION)
- Next.js 9 (VERSION)
- next (VERSION)
- next.js (VERSION)
- next.js 14 (VERSION)
- nextjs (VERSION)
- nextjs 14 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Vercel
- License
- mit
- Year introduced
- 2016
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Next.js appears in many frontend/full-stack job descriptions and is a common React meta-framework for production apps; Vercel’s ecosystem and strong GitHub adoption signal broad market demand.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Meta-Frameworks & SSR Catalog dimension db id 130
Library dimension (catalog)
Roles linked in library: Frontend Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Meta-Frameworks & SSR
meta-frameworks-ssr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Tailwind CSS (CANONICAL) primary
- tailwind (VERSION)
- tailwind 3 (VERSION)
- tailwind 3.x (VERSION)
- tailwind css (VERSION)
- tailwind v3 (VERSION)
- tailwindcss (VERSION)
- tailwindcss v3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Css Framework
- Vendor
- Tailwind Labs
- License
- mit
- Year introduced
- 2017
- Confidence
- 0.97
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Widely listed in frontend job descriptions and used across many production web stacks; strong GitHub adoption and ecosystem support indicate it’s a hiring-pipeline staple.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 481
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CSS Architecture and Styling Catalog dimension db id 117
Library dimension (catalog)
Roles linked in library: Frontend Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CSS Architecture and Styling
css-architecture-and-styling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- RAG (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Retrieval Augmented Generation
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: RAG appears in many recent AI/ML job descriptions and vendor docs, but it is still not a universal baseline skill like Python or SQL; market demand is rising fast rather than fully standardized.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 904
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Job postings increasingly mention agentic workflows alongside LLM orchestration and tool-use, and GitHub activity around agent frameworks has surged, but it is not yet a universal hiring staple.
(0.60)
“Agentic workflows” is a specific architecture concept; typical JDs won’t confuse it with other distinct catalog skills.
Not versioned
Architecture ·agentic_workflow_architecture confidence 0.88
By the Architecture vs Concept rule, agentic workflows describe a system-shape/pattern for how autonomous agents are organized and interact, rather than a single knowledge unit or process.
- Category
- Architecture
- Sub-category
- agentic_workflow_architecture
- Skill nature
- PATTERN
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Agentic Workflow Orchestration
Pipeline tentative id
Designing and coordinating multi-step AI agent processes that plan, act, observe results, and adapt toward a goal. This covers workflow patterns where an LLM-driven agent uses tools, memory, and control logic to complete tasks autonomously or semi-autonomously.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Vector DBs are increasingly listed in AI/ML job descriptions and vendor ecosystems, but they’re not yet a universal datastore staple like PostgreSQL or AWS.
Pinecone ·proprietary ·since 2020 (0.85)
“Vector DB” in JDs typically refers specifically to vector databases for embeddings/search, not other datastore types.
Not versioned
Datastore ·vector_database confidence 0.93
By the Datastore vs Format rule, a vector DB is a system that persists and indexes data for retrieval, so it is fundamentally a Datastore rather than a Tool or Platform.
- Category
- Datastore
- Sub-category
- vector_database
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Vector Databases and Similarity Search
Pipeline tentative id
Datastores optimized for storing embeddings and performing nearest-neighbor similarity search over high-dimensional vectors. Vector DB belongs here because it refers to the database layer used for retrieval, indexing, and semantic search in AI systems.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Hybrid search appears increasingly in job descriptions for RAG/search roles and is supported by major vendors like Elasticsearch and OpenSearch, but it is not yet a universal hiring staple.
(0.95)
“Hybrid Search” is a specific retrieval pattern (combining lexical + vector). Typical JDs won’t confuse it with other distinct search/IR skills in the catalog.
Not versioned
Concept ·search_retrieval_pattern confidence 0.92
Hybrid Search is fundamentally a named retrieval concept/pattern combining multiple search approaches, and it is not a tool, platform, or architecture under the provided disambiguation rules.
- Category
- Concept
- Sub-category
- search_retrieval_pattern
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Hybrid Search Retrieval
Pipeline tentative id
Retrieval approaches that combine lexical and semantic search signals to improve relevance. Hybrid search belongs here because it typically blends keyword matching with vector similarity, reranking, and query fusion for robust information retrieval.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Prompt engineering is increasingly listed in AI/LLM job descriptions and vendor docs, but it’s still not a universal hiring staple like Python or AWS.
(0.95)
“Prompt engineering” is a specific, commonly used term for LLM prompting and is unlikely to be confused with other catalog skills.
Not versioned
Methodology ·prompt_engineering confidence 0.93
Prompt engineering is fundamentally a way of working for crafting and iterating prompts, so by the Concept vs Methodology rule it fits Methodology rather than a tool or concept.
- Category
- Methodology
- Sub-category
- prompt_engineering
- Skill nature
- METHODOLOGY
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Prompt Engineering
Pipeline tentative id
Designing, refining, and evaluating prompts for large language models and other generative AI systems. This includes instruction phrasing, few-shot examples, output constraints, and iterative prompt debugging to improve reliability and task performance.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- OpenAI (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Ai Platform
- Vendor
- OpenAI
- License
- other_open
- Year introduced
- 2015
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: OpenAI appears in a growing number of job postings for LLM/app integration, but it is not yet a universal baseline skill like AWS or Python; market demand is rising alongside API adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 896
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Anthropic (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Ai Model Platform
- Vendor
- Anthropic
- License
- unknown
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Anthropic/Claude is increasingly listed in AI engineer JDs and vendor docs, but it is not yet as universal as AWS/OpenAI; GitHub and job-market signals show rapid growth rather than saturation.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 898
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
AWS Bedrock is appearing in more job descriptions and vendor docs as teams adopt managed LLM APIs, but it is still far less common than core AWS services like EC2/S3 or Kubernetes in hiring pipelines.
Amazon Web Services ·proprietary ·since 2023 (0.95)
AWS Bedrock is a specific AWS foundation-model service; typical JDs distinguish it from other AWS AI services.
Not versioned
Service ·foundation_model_service confidence 0.97
By the Platform vs Service rule, AWS Bedrock is a managed capability inside AWS rather than the AWS platform itself, so it is a Service.
- Category
- Service
- Sub-category
- foundation_model_service
- Skill nature
- CLOUD_SERVICE
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
Locked dimensions (v3 placement)
-
Cloud Platform Services
Reuses catalog slug
Core managed services offered by major cloud providers for building and operating applications. AWS Bedrock fits here because it is an AWS-managed platform service used by engineers to access foundation models and related AI capabilities.
-
Cloud Platforms
Reuses catalog slug
Proficiency in major cloud service provider platforms and their core services.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- LangChain (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Llm Application Framework
- Vendor
- Harrison Chase
- License
- mit
- Year introduced
- 2022
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LangChain appears in many recent AI/LLM job postings and is widely used in app prototypes, but it’s still not a universal hiring staple like React or AWS.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 146
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Operations and Orchestration Catalog dimension db id 49
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
DSPy is appearing in more LLM engineering job descriptions and GitHub adoption is rising, but it is still far from a universal hiring staple like PyTorch or LangChain.
Cohere ·apache_2 ·since 2023 (0.90)
DSPy is a specific LLM programming framework name; unlikely to be confused with other catalog skills.
Not versioned
Framework ·llm_programming_framework confidence 0.90
DSPy is best classified as a Framework because users build applications and LLM pipelines inside it rather than using it as standalone software.
- Category
- Framework
- Sub-category
- llm_programming_framework
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Prompt Programming Frameworks
Pipeline tentative id
Frameworks and libraries for building, optimizing, and evaluating LLM applications through structured prompts, modules, and programmatic prompt composition. DSPy belongs here because it is a prompt-centric framework for writing and tuning LLM pipelines rather than a general ML model library.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Docker (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Containerization Tool
- Vendor
- Docker, Inc.
- License
- apache_2
- Year introduced
- 2013
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Docker is a hiring-pipeline staple: it appears in many DevOps, backend, and platform JDs, and remains a standard containerization tool alongside Kubernetes in production stacks.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 654
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Containerization and Image Builds Catalog dimension db id 152
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Runtime Configuration Catalog dimension db id 13
Library dimension (catalog)
Roles linked in library: Backend Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Containerization and Image Builds
containerization-and-image-builds
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Kubernetes (CANONICAL) primary
- Kubernetes 1.0+ (VERSION)
- Kubernetes 1.x (VERSION)
- Kubernetes v1 (VERSION)
- k8s (VERSION)
- kubernetes 1.x (VERSION)
- kubernetes latest (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Container Orchestration Platform
- Vendor
- Cloud Native Computing Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 1.30
Maturity reasoning: Broadly adopted in cloud-native stacks; Kubernetes appears in a large share of DevOps/SRE job descriptions and is the default orchestration platform across major cloud vendors.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 557
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Amazon Redshift is widely listed in data-warehouse/cloud analytics job descriptions and remains an AWS flagship service; no vendor sunset, and it’s commonly paired with Snowflake/BigQuery rather than replaced.
Amazon ·proprietary ·since 2012 (0.95)
“Redshift” typically refers specifically to Amazon Redshift; it’s unlikely to be confused with other distinct data-warehouse platforms in typical JDs.
Not versioned
Platform ·data_warehouse_platform confidence 0.93
By the Vendor SaaS = Platform rule, Amazon Redshift is a hosted multi-tenant managed analytics environment with APIs rather than software you run yourself, so it fits Platform.
- Category
- Platform
- Sub-category
- data_warehouse_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
Locked dimensions (v3 placement)
-
Cloud Data Warehousing Platforms
Reuses catalog slug
Managed cloud data warehouse services used for analytical storage, SQL querying, and large-scale reporting. Redshift belongs here because it is an AWS-managed warehouse platform for storing and analyzing structured data.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
AWS RDS is a standard managed database service and appears frequently in cloud/DevOps job descriptions alongside PostgreSQL/MySQL on AWS, indicating broad hiring-pipeline adoption.
Amazon Web Services ·proprietary ·since 2009 (0.95)
Could be confused with: rds_aws
“RDS” is commonly used for AWS Relational Database Service; could be extracted as a specific AWS RDS skill vs a generic RDS entry.
Not versioned
Service ·managed_relational_database_service confidence 0.97
By the Service vs Platform rule, RDS is a specific managed capability inside AWS rather than the whole hosted environment, so it is a Service.
- Category
- Service
- Sub-category
- managed_relational_database_service
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
Locked dimensions (v3 placement)
-
Cloud Database Services
Reuses catalog slug
Managed database services offered by cloud providers for relational storage, scaling, backups, and high availability. RDS belongs here because it commonly refers to Amazon Relational Database Service, a core managed database platform in cloud engineering.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Amazon S3 is a default cloud storage service in AWS job descriptions and architecture docs; it remains broadly adopted with no vendor sunset, and is commonly paired with S3-compatible storage rather than replaced.
Amazon ·proprietary ·since 2006 (0.95)
“S3” in JDs typically refers unambiguously to AWS Simple Storage Service; other common meanings are rare in this context.
Not versioned
Platform ·cloud_storage_platform confidence 0.90
By the Platform vs Tool rule, S3 is a hosted multi-tenant AWS environment with APIs and managed storage capabilities, so it is a Platform rather than a user-run tool or a datastore product.
- Category
- Platform
- Sub-category
- cloud_storage_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Systems Programming Catalog dimension db id 166
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Cloud Storage Services
Reuses catalog slug
Managed object storage services used to store, retrieve, and serve files, datasets, backups, and application assets. S3 belongs here because it is the canonical AWS object storage service and is commonly used as foundational cloud storage infrastructure.
-
Object Storage Operations
Pipeline tentative id
Operational use of object storage for durable file and dataset management across applications and pipelines. This fits S3 when the emphasis is on organizing objects, controlling access, and managing storage behavior rather than broader cloud platform knowledge.
-
AWS Storage Services
Pipeline tentative id
AWS-specific storage primitives and managed storage offerings used to persist application data and artifacts. S3 belongs here as the primary AWS object storage service and a common integration point for AI and data workflows.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Systems Programming
d_init_02
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Redis (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Key Value Store
- Vendor
- Redis Labs
- License
- apache_2
- Year introduced
- 2009
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Redis appears in many job descriptions for caching, queues, and session storage, and is a standard datastore in modern backend stacks; vendor activity and broad ecosystem support indicate strong market demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 28
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Caching and State Management Catalog dimension db id 7
Library dimension (catalog)
Roles linked in library: Backend Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Caching and State Management
caching-and-state-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Vector search is increasingly listed in AI/ML and search JDs, and major vendors like Pinecone, Weaviate, and pgvector show strong adoption, but it is not yet a universal hiring staple.
(0.95)
“Vector Search” is a specific retrieval concept (embeddings + similarity) and is unlikely to be confused with other catalog skills.
Not versioned
Concept ·vector_search confidence 0.78
Vector Search is best treated as a Concept because it names a retrieval approach/technique rather than a specific product, runtime, or system you operate.
- Category
- Concept
- Sub-category
- vector_search
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Vector Search Systems
Pipeline tentative id
Systems and techniques for indexing, storing, and querying embeddings by semantic similarity. Vector Search belongs here because it is the core retrieval mechanism behind embedding-based search, recommendation, and RAG pipelines.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Git (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Version Control Tool
- Vendor
- Linus Torvalds
- License
- gpl_v2
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 730
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Azure DevOps appears in many enterprise job descriptions for CI/CD, boards, and repos, and Microsoft continues active product support and updates; it remains a common hiring-pipeline skill alongside GitHub Actions/Jenkins.
Microsoft ·proprietary ·since 2018 (0.95)
“Azure DevOps” is a specific Microsoft DevOps suite; typical JDs won’t confuse it with other CI/CD platforms like Jenkins or GitHub Actions.
Not versioned
Platform ·devops_platform confidence 0.93
By the Platform vs Tool rule, Azure DevOps is a hosted multi-tenant environment with APIs and managed services rather than software you run yourself.
- Category
- Platform
- Sub-category
- devops_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
Locked dimensions (v3 placement)
-
CI/CD Pipeline Platforms
Reuses catalog slug
Systems used to define, run, and maintain automated build and deployment workflows. Azure DevOps belongs here because it provides pipeline authoring, build agents, release automation, and repo-integrated delivery tooling.
-
CI/CD Pipeline Platforms
Reuses catalog slug
Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Agile (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 367
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Scrum appears in a large share of software PM/Agile job descriptions and is a standard certification/topic in hiring pipelines, indicating broad market adoption.
(0.95)
“Scrum” is a specific Agile framework; typical JDs distinguish it from other methodologies like Kanban or XP.
Not versioned
Methodology ·agile_project_management_methodology confidence 0.99
By the Concept vs Methodology rule, Scrum is a way of working and managing work rather than a knowledge unit or system shape, so it is a Methodology.
- Category
- Methodology
- Sub-category
- agile_project_management_methodology
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Agile Scrum Practices
Pipeline tentative id
Scrum is an agile delivery framework for planning, coordinating, and inspecting work in iterative increments. It belongs here because it defines team ceremonies, roles, and backlog-driven execution rather than a technical implementation skill.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
DevOps appears in a large share of software and platform engineering job descriptions, often alongside CI/CD, Kubernetes, and cloud tooling; it is a standard hiring-pipeline keyword rather than a niche specialty.
(0.95)
“DevOps” is a widely used, distinct methodology term; typical JDs won’t confuse it with other specific catalog skills.
Not versioned
Methodology ·devops_methodology confidence 0.97
DevOps is fundamentally a way of working that combines development and operations practices, so by the Concept vs Methodology rule it is a Methodology.
- Category
- Methodology
- Sub-category
- devops_methodology
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Deployment and Release Patterns Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Release Patterns Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Cloud Architect
Locked dimensions (v3 placement)
-
CI/CD Pipeline Platforms
Reuses catalog slug
Systems used to define, run, and maintain automated build, test, and deployment workflows. DevOps work commonly centers on these delivery pipelines and the tooling that operationalizes software changes.
-
Infrastructure as Code
Reuses catalog slug
Declarative provisioning and environment definition tools used to codify cloud infrastructure and repeatable platform setup. DevOps often includes IaC because it automates the environments that delivery pipelines deploy into.
-
Deployment and Release Patterns
Reuses catalog slug
Patterns for promoting software safely across environments, including rollout, rollback, gating, and release coordination. DevOps frequently includes these operational release practices because they govern how changes reach production.
-
Infrastructure as Code
Reuses catalog slug
Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.
-
CI/CD Pipeline Platforms
Reuses catalog slug
Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.
-
Deployment and Release Patterns
Reuses catalog slug
Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code
infrastructure-as-code
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- CI/CD (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Ci Cd Process
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 900
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
IAM is a standard cloud/security platform skill; it appears routinely in AWS/Azure/GCP job descriptions and is a core control in vendor docs and compliance frameworks, indicating broad hiring demand.
Amazon Web Services ·proprietary ·since 2011 (0.90)
IAM (Identity and Access Management) is a standard, specific security domain; typical JDs won’t confuse it with other catalog skills.
Not versioned
Platform ·identity_and_access_management_platform confidence 0.90
By the Vendor SaaS = Platform rule, IAM here is best treated as a hosted identity and access management environment with APIs rather than a local tool, since it denotes the managed access-control platform capability.
- Category
- Platform
- Sub-category
- identity_and_access_management_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Identity and Access Management
Pipeline tentative id
Controls for defining identities, roles, permissions, and access policies across systems. IAM belongs here because it is the core discipline for authenticating principals and authorizing what they can do.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Monitoring is a standard requirement in most SRE/DevOps job descriptions and is bundled into major platforms like AWS CloudWatch, Datadog, and Prometheus, indicating broad market adoption.
(1.00)
“Monitoring” in observability/incident triage is a common, specific concept and is unlikely to be confused with other distinct catalog skills.
Not versioned
Concept ·observability_monitoring confidence 0.88
Monitoring is fundamentally a knowledge unit about observing system health and behavior, so it fits the Concept category rather than a Tool or Platform under the provided rules.
- Category
- Concept
- Sub-category
- observability_monitoring
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Observability and Incident Triage Catalog dimension db id 155
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Observability and Incident Triage Catalog dimension db id 155
Library dimension (catalog)
Roles linked in library: DevOps Engineer
Locked dimensions (v3 placement)
-
Observability and Incident Triage
Reuses catalog slug
Telemetry, alerting, and troubleshooting practices used to detect and diagnose unhealthy systems. Monitoring belongs here because it is the core activity of watching metrics, logs, and traces to spot issues and drive response.
-
Observability and Incident Triage
Reuses catalog slug
Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Observability and Incident Triage
observability-and-incident-triage
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Load balancing is a standard architecture requirement in cloud and infra JDs, commonly listed alongside AWS, Kubernetes, and NGINX/HAProxy for production traffic distribution.
(0.95)
“Load Balancing” in JDs typically refers to distributing traffic across instances; it’s distinct from other architecture skills in the catalog.
Not versioned
Architecture ·traffic_distribution_architecture confidence 0.88
Load balancing is fundamentally a system-shape pattern for distributing traffic across multiple instances, so it fits the Architecture category rather than a tool or concept.
- Category
- Architecture
- Sub-category
- traffic_distribution_architecture
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Deployment and Release Patterns Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Deployment and Release Patterns Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Cloud Architect
Locked dimensions (v3 placement)
-
Deployment and Release Patterns
Reuses catalog slug
Patterns for distributing traffic safely across application instances and environments during rollout and steady-state operation. Load balancing belongs here when it is used to spread requests, improve availability, and support controlled release behavior.
-
Deployment and Release Patterns
Reuses catalog slug
Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- autoscaling (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Scaling Concept
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Autoscaling is a standard cloud/Kubernetes capability and appears routinely in AWS, GCP, Azure, and Kubernetes job descriptions, with vendor docs and managed services built around it.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 604
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Amazon ECR is a standard AWS container registry; it appears frequently in cloud/platform job descriptions and is the default registry in many Kubernetes/ECS deployment stacks.
Amazon ·proprietary ·since 2015 (0.95)
ECR is a specific, commonly referenced AWS service name (Elastic Container Registry), unlikely to be confused with other catalog skills.
Not versioned
Platform ·container_registry_platform confidence 0.90
By the Platform vs Tool rule, ECR is a hosted, multi-tenant AWS-managed registry service consumed via APIs rather than software you run yourself.
- Category
- Platform
- Sub-category
- container_registry_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
Locked dimensions (v3 placement)
-
Cloud Container Registry Services
Reuses catalog slug
Managed registry services used to store, version, scan, and distribute container images and related artifacts. ECR belongs here because it is AWS's container registry used by build and deployment workflows.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
AKS appears frequently in cloud/Kubernetes job descriptions and Microsoft actively markets it as a core Azure service, indicating broad enterprise adoption rather than niche use.
Microsoft ·other_open ·since 2018 (0.90)
AKS is a specific acronym for Azure Kubernetes Service; typical JDs won’t confuse it with other Kubernetes platforms.
Not versioned
Platform ·kubernetes_platform confidence 0.97
AKS is a vendor-hosted managed Kubernetes environment with APIs and multi-tenancy, so by the Platform vs Tool rule it is a Platform rather than software you run yourself.
- Category
- Platform
- Sub-category
- kubernetes_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
Locked dimensions (v3 placement)
-
Container Orchestration Platforms
Reuses catalog slug
Platforms that schedule, scale, and manage containerized workloads across clusters and environments. AKS belongs here because it is Azure's managed Kubernetes service used to run and operate container workloads.
-
Container Orchestration Platforms
Reuses catalog slug
Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Azure Container Registry (ACR) is a standard Azure service commonly listed in cloud/container DevOps job descriptions alongside AKS and Docker; Microsoft continues active support and docs, indicating broad market adoption.
Microsoft ·other_open ·since 2017 (0.90)
Could be confused with: acr_azure_container_registry, acr_aws_cloudfront_origin_request_control
ACR is a common acronym; in JDs it may refer to Azure Container Registry or other ACR-related services, not uniquely this platform.
Not versioned
Platform ·container_registry_platform confidence 0.88
By the Platform vs Tool rule, ACR (Azure Container Registry) is a hosted, multi-tenant Azure service with APIs rather than software you run yourself, so it fits Platform.
- Category
- Platform
- Sub-category
- container_registry_platform
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer
Locked dimensions (v3 placement)
-
Azure Container Registry
Pipeline tentative id
Azure Container Registry (ACR) is the managed registry used to store, version, and distribute container images and related artifacts in Azure-centric delivery flows. It belongs here because ACR is the specific platform skill, not the broader container orchestration or security domains.
-
Cloud Platforms
Reuses catalog slug
Cloud platforms cover major provider ecosystems and their managed services used to build and operate applications. ACR fits here as an Azure-managed service within the broader cloud platform surface.
-
Cloud Platforms
Reuses catalog slug
Proficiency in major cloud service provider platforms and their core services.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Django | in_db |
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CloudFormation | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| React | in_db |
UI Frameworks and Rendering
ui-frameworks-and-rendering
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Next.js | in_db |
Meta-Frameworks & SSR
meta-frameworks-ssr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Tailwind CSS | in_db |
CSS Architecture and Styling
css-architecture-and-styling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RAG | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| OpenAI | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Anthropic | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| LangChain | in_db |
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Docker | in_db |
Containerization and Image Builds
containerization-and-image-builds
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Docker | in_db |
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kubernetes | in_db |
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Redis | in_db |
Caching and State Management
caching-and-state-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Git | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Autoscaling | in_db |
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| FastAPI | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Databricks | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Unity Catalog | in_db |
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Unity Catalog | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agentic workflows | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Vector DB | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hybrid Search | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prompt engineering | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS Bedrock | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DSPy | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Redshift | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RDS | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| S3 | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| S3 | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| S3 | in_db |
Systems Programming
d_init_02
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Vector Search | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure DevOps | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Scrum | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| IAM | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Monitoring | in_db |
Observability and Incident Triage
observability-and-incident-triage
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Load Balancing | in_db |
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| ECR | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AKS | in_db |
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| ACR | in_db |
React Frontend Development
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| ACR | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_added | FastAPI | 1201 |
| canonical_skill_added | Databricks | 1202 |
| canonical_skill_added | Unity Catalog | 1203 |
| canonical_skill_added | Agentic workflows | 1204 |
| canonical_skill_added | Vector DB | 1205 |
| canonical_skill_added | Hybrid Search | 1206 |
| canonical_skill_added | Prompt engineering | 1207 |
| canonical_skill_added | AWS Bedrock | 1208 |
| canonical_skill_added | DSPy | 1209 |
| canonical_skill_added | Redshift | 1210 |
| canonical_skill_added | RDS | 1211 |
| canonical_skill_added | S3 | 1212 |
| canonical_skill_added | Vector Search | 1213 |
| canonical_skill_added | Azure DevOps | 1214 |
| canonical_skill_added | Scrum | 1215 |
| canonical_skill_added | DevOps | 1216 |
| canonical_skill_added | IAM | 1217 |
| canonical_skill_added | Monitoring | 1218 |
| canonical_skill_added | Load Balancing | 1219 |
| canonical_skill_added | ECR | 1220 |
| canonical_skill_added | AKS | 1221 |
| canonical_skill_added | ACR | 1222 |
| dimension_skill_link | FastAPI ↔ React Frontend Development | 96 |
| dimension_skill_link | Databricks ↔ React Frontend Development | 96 |
| dimension_skill_link | Unity Catalog ↔ Data Lineage and Metadata | 28 |
| dimension_skill_link | Unity Catalog ↔ React Frontend Development | 96 |
| dimension_skill_link | Agentic workflows ↔ React Frontend Development | 96 |
| dimension_skill_link | Vector DB ↔ React Frontend Development | 96 |
| dimension_skill_link | Hybrid Search ↔ React Frontend Development | 96 |
| dimension_skill_link | Prompt engineering ↔ React Frontend Development | 96 |
| dimension_skill_link | AWS Bedrock ↔ Cloud Platforms | 20 |
| dimension_skill_link | DSPy ↔ React Frontend Development | 96 |
| dimension_skill_link | Redshift ↔ Cloud Platforms | 20 |
| dimension_skill_link | RDS ↔ Cloud Platforms | 20 |
| dimension_skill_link | S3 ↔ Cloud Platforms | 20 |
| dimension_skill_link | S3 ↔ React Frontend Development | 96 |
| dimension_skill_link | S3 ↔ Systems Programming | 166 |
| dimension_skill_link | Vector Search ↔ React Frontend Development | 96 |
| dimension_skill_link | Azure DevOps ↔ CI/CD Pipeline Platforms | 150 |
| dimension_skill_link | Scrum ↔ React Frontend Development | 96 |
| dimension_skill_link | DevOps ↔ CI/CD Pipeline Platforms | 150 |
| dimension_skill_link | DevOps ↔ Infrastructure as Code | 132 |
| dimension_skill_link | DevOps ↔ Deployment and Release Patterns | 140 |
| dimension_skill_link | IAM ↔ React Frontend Development | 96 |
| dimension_skill_link | Monitoring ↔ Observability and Incident Triage | 155 |
| dimension_skill_link | Load Balancing ↔ Deployment and Release Patterns | 140 |
| dimension_skill_link | ECR ↔ Cloud Platforms | 20 |
| dimension_skill_link | AKS ↔ Container Orchestration Platforms | 134 |
| dimension_skill_link | ACR ↔ React Frontend Development | 96 |
| dimension_skill_link | ACR ↔ Cloud Platforms | 20 |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": {
"currency": "INR",
"max": 1.3,
"min": 1.2,
"period": "monthly",
"raw": "1.2-1.3 LPM"
},
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science (or related)",
"raw": "Bachelor\u2019s degree in computer science, Information Technology, or a related field (or equivalent work experience).",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 3,
"raw": "3+ years"
},
"job_locations": [
{
"aliases": [],
"city": "Noida",
"country": null,
"state": null,
"work_mode": "onsite"
},
{
"aliases": [],
"city": "Pune",
"country": null,
"state": null,
"work_mode": "onsite"
},
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": null,
"state": null,
"work_mode": "onsite"
}
],
"role": "AI Engineer",
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "What They Will Do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Take ownership of architecture design",
"last_5_words": "design and best practices."
},
"text": "Take ownership of architecture design and development of scalable and distributed software systems.\nTranslate business to technical requirements\nOwn technical execution, ensuring code quality, adherence to deadlines, and efficient resource allocation\nData driven decision making skills with focus on achieving product goals\nDesign, develop and deploy LLM based pipelines involving patterns like RAG, Agentic workflows,\nResponsible for the complete software development lifecycle, including requirements analysis, design, coding, testing, and deployment.\nUtilize AWS services/ Azure services like IAM, Monitoring, Load Balancing, Autoscaling, Database, Networking, storage, ECR, AKS, ACR etc.\nUtilize Databricks capabilities, esp. Unity Catalog and be able to architect the governance layer from data all-the-way to the AI model output\nDesign, develop and deploy prompt and response guardrails to enable responsible AI requirements\nImplement DevOps practices using tools like Docker, Kubernetes to ensure continuous integration and delivery. Develop DevOps scripts for automation and monitoring.\nCollaborate with cross-functional teams, conduct code reviews, and provide guidance on software design and best practices.",
"word_count": 218
},
{
"bullet_count": 0,
"heading": "What They Will Bring",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Bachelor\u2019s degree in computer science",
"last_5_words": "development and delivery"
},
"text": "Bachelor\u2019s degree in computer science, Information Technology, or a related field (or equivalent work experience).\nStrong coding skills with proficiency in Python\nExperience with API frameworks both stateless and stateful such as Fast API, Django\nProficient in cloud platforms, specifically AWS, Databricks and Azure\nProficient in Infra-as-Code, esp. focused on developing Terraform scripts and Cloudformation\nKnowledge and hands-on experience with front-end development (React JS, Next JS, Tailwind CSS) preferred\nStrong experience in LLM patterns like RAG, Vector DB, Hybrid Search, Agent development, Agentic workflows, prompt engineering, etc.\nStrong experience with LLM APIs (Open AI, Anthropic, AWS Bedrock), SDKs (Langchain, DSPy)\nHands-on experience with DevOps tools including Docker, Kubernetes, and AWS services (Redshift, RDS, S3).\nHands-on experience with Databricks solutions including Unity Catalog.\nExperience in production deployments involving thousands of users, esp with capabilities like Redis, Vector Search, etc.\nStrong understanding of scalable application design principles and experience with security best practices and compliance with privacy regulations.\nGood knowledge of software engineering practices like version control (GIT), DevOps (Azure DevOps preferred) and Agile or Scrum.\nStrong communication skills, with the ability to effectively convey complex technical concepts to a diverse audience.\nExperience of SDLC and best practices while development\nExperience with Agile methodology for continuous product development and delivery",
"word_count": 263
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "FastAPI"
},
{
"is_primary": true,
"skill_name": "Django"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "Azure"
},
{
"is_primary": true,
"skill_name": "Databricks"
},
{
"is_primary": true,
"skill_name": "Unity Catalog"
},
{
"is_primary": true,
"skill_name": "Terraform"
},
{
"is_primary": true,
"skill_name": "CloudFormation"
},
{
"is_primary": false,
"skill_name": "React"
},
{
"is_primary": false,
"skill_name": "Next.js"
},
{
"is_primary": false,
"skill_name": "Tailwind CSS"
},
{
"is_primary": true,
"skill_name": "RAG"
},
{
"is_primary": true,
"skill_name": "Agentic workflows"
},
{
"is_primary": true,
"skill_name": "Vector DB"
},
{
"is_primary": true,
"skill_name": "Hybrid Search"
},
{
"is_primary": true,
"skill_name": "Prompt engineering"
},
{
"is_primary": true,
"skill_name": "OpenAI"
},
{
"is_primary": true,
"skill_name": "Anthropic"
},
{
"is_primary": true,
"skill_name": "AWS Bedrock"
},
{
"is_primary": true,
"skill_name": "LangChain"
},
{
"is_primary": true,
"skill_name": "DSPy"
},
{
"is_primary": true,
"skill_name": "Docker"
},
{
"is_primary": true,
"skill_name": "Kubernetes"
},
{
"is_primary": false,
"skill_name": "Redshift"
},
{
"is_primary": false,
"skill_name": "RDS"
},
{
"is_primary": false,
"skill_name": "S3"
},
{
"is_primary": false,
"skill_name": "Redis"
},
{
"is_primary": false,
"skill_name": "Vector Search"
},
{
"is_primary": true,
"skill_name": "Git"
},
{
"is_primary": false,
"skill_name": "Azure DevOps"
},
{
"is_primary": true,
"skill_name": "Agile"
},
{
"is_primary": false,
"skill_name": "Scrum"
},
{
"is_primary": true,
"skill_name": "DevOps"
},
{
"is_primary": true,
"skill_name": "CI/CD"
},
{
"is_primary": false,
"skill_name": "IAM"
},
{
"is_primary": false,
"skill_name": "Monitoring"
},
{
"is_primary": false,
"skill_name": "Load Balancing"
},
{
"is_primary": false,
"skill_name": "Autoscaling"
},
{
"is_primary": false,
"skill_name": "ECR"
},
{
"is_primary": false,
"skill_name": "AKS"
},
{
"is_primary": false,
"skill_name": "ACR"
}
],
"jd_role": {
"display_name": "AI Engineer",
"rationale": null,
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": {
"currency": "INR",
"max": 1.3,
"min": 1.2,
"period": "monthly",
"raw": "1.2-1.3 LPM"
},
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science (or related)",
"raw": "Bachelor\u2019s degree in computer science, Information Technology, or a related field (or equivalent work experience).",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 3,
"raw": "3+ years"
},
"job_locations": [
{
"aliases": [],
"city": "Noida",
"country": null,
"state": null,
"work_mode": "onsite"
},
{
"aliases": [],
"city": "Pune",
"country": null,
"state": null,
"work_mode": "onsite"
},
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": null,
"state": null,
"work_mode": "onsite"
}
],
"role": "AI Engineer",
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "What They Will Do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Take ownership of architecture design",
"last_5_words": "design and best practices."
},
"text": "Take ownership of architecture design and development of scalable and distributed software systems.\nTranslate business to technical requirements\nOwn technical execution, ensuring code quality, adherence to deadlines, and efficient resource allocation\nData driven decision making skills with focus on achieving product goals\nDesign, develop and deploy LLM based pipelines involving patterns like RAG, Agentic workflows,\nResponsible for the complete software development lifecycle, including requirements analysis, design, coding, testing, and deployment.\nUtilize AWS services/ Azure services like IAM, Monitoring, Load Balancing, Autoscaling, Database, Networking, storage, ECR, AKS, ACR etc.\nUtilize Databricks capabilities, esp. Unity Catalog and be able to architect the governance layer from data all-the-way to the AI model output\nDesign, develop and deploy prompt and response guardrails to enable responsible AI requirements\nImplement DevOps practices using tools like Docker, Kubernetes to ensure continuous integration and delivery. Develop DevOps scripts for automation and monitoring.\nCollaborate with cross-functional teams, conduct code reviews, and provide guidance on software design and best practices.",
"word_count": 218
},
{
"bullet_count": 0,
"heading": "What They Will Bring",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Bachelor\u2019s degree in computer science",
"last_5_words": "development and delivery"
},
"text": "Bachelor\u2019s degree in computer science, Information Technology, or a related field (or equivalent work experience).\nStrong coding skills with proficiency in Python\nExperience with API frameworks both stateless and stateful such as Fast API, Django\nProficient in cloud platforms, specifically AWS, Databricks and Azure\nProficient in Infra-as-Code, esp. focused on developing Terraform scripts and Cloudformation\nKnowledge and hands-on experience with front-end development (React JS, Next JS, Tailwind CSS) preferred\nStrong experience in LLM patterns like RAG, Vector DB, Hybrid Search, Agent development, Agentic workflows, prompt engineering, etc.\nStrong experience with LLM APIs (Open AI, Anthropic, AWS Bedrock), SDKs (Langchain, DSPy)\nHands-on experience with DevOps tools including Docker, Kubernetes, and AWS services (Redshift, RDS, S3).\nHands-on experience with Databricks solutions including Unity Catalog.\nExperience in production deployments involving thousands of users, esp with capabilities like Redis, Vector Search, etc.\nStrong understanding of scalable application design principles and experience with security best practices and compliance with privacy regulations.\nGood knowledge of software engineering practices like version control (GIT), DevOps (Azure DevOps preferred) and Agile or Scrum.\nStrong communication skills, with the ability to effectively convey complex technical concepts to a diverse audience.\nExperience of SDLC and best practices while development\nExperience with Agile methodology for continuous product development and delivery",
"word_count": 263
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "c2572c5b-9053-41b1-b0ec-8b73981437bd",
"stage3_signals": {
"alias_match_roles": [
{
"display_name": "AI Engineer",
"matched_count": null,
"role_id": 13,
"score": 1.0,
"slug": "ai-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "AI Compliance Officer",
"matched_count": null,
"role_id": 12,
"score": 0.4931,
"slug": "ai-compliance-officer",
"total_count": null
},
{
"display_name": "AR/VR Engineer",
"matched_count": null,
"role_id": 8,
"score": 0.4711,
"slug": "ar-vr-engineer",
"total_count": null
},
{
"display_name": "Android Engineer",
"matched_count": null,
"role_id": 4,
"score": 0.4616,
"slug": "android-engineer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"matched_count": null,
"role_id": 9,
"score": 0.4567,
"slug": "cloud-architect",
"total_count": null
},
{
"display_name": "Backend Engineer",
"matched_count": null,
"role_id": 1,
"score": 0.4563,
"slug": "backend-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "DevOps Engineer",
"matched_count": 8,
"role_id": 10,
"score": 0.1905,
"slug": "devops-engineer",
"total_count": 42
},
{
"display_name": "Backend Engineer",
"matched_count": 6,
"role_id": 1,
"score": 0.1429,
"slug": "backend-engineer",
"total_count": 42
},
{
"display_name": "Cloud Architect",
"matched_count": 6,
"role_id": 9,
"score": 0.1429,
"slug": "cloud-architect",
"total_count": 42
},
{
"display_name": "ML Engineer",
"matched_count": 6,
"role_id": 3,
"score": 0.1429,
"slug": "ml-engineer",
"total_count": 42
},
{
"display_name": "Frontend Engineer",
"matched_count": 3,
"role_id": 7,
"score": 0.0714,
"slug": "frontend-engineer",
"total_count": 42
}
],
"stage35_ran": false
},
"stage4_decision": {
"alias_collision_detected": true,
"case": "D",
"chosen_role": {
"display_name": "AI Engineer",
"matched_count": null,
"role_id": 13,
"score": 1.0,
"slug": "ai-engineer",
"total_count": null
},
"confidence": 0.95,
"llm2_fired": true,
"llm2_reasoning": "The JD\u2019s emphasis on end-to-end architecture, scalable software development, LLM pipelines, cloud/DevOps, and prompt engineering aligns directly with the day-to-day responsibilities of an AI Engineer rather than an AI Compliance Officer.",
"queued": false,
"reasoning": "LLM2 picked ai-engineer (confidence 0.95)"
},
"stage5_updates": {
"centroid_n_after": 2,
"centroid_updated": true,
"collision_log_id": 10,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 6,
"r_and_r_preview": "Take ownership of architecture design and development of scalable and distributed software systems.\nTranslate business to technical requirements\nOwn technical execution, ensuring code quality, adheren",
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 79,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "FastAPI",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 80,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Databricks",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 81,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Unity Catalog",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 82,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Agentic workflows",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 83,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Vector DB",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 84,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Hybrid Search",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 85,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Prompt engineering",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 86,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "AWS Bedrock",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 87,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "DSPy",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 88,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Redshift",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 89,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "RDS",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 90,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "S3",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 91,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Vector Search",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 92,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Azure DevOps",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 93,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Scrum",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 94,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "DevOps",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 95,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "IAM",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 96,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Monitoring",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 97,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "Load Balancing",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 98,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "ECR",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 99,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "AKS",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 100,
"role_display_name": "AI Engineer",
"role_slug": "ai-engineer",
"skill_name": "ACR",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
"category_id": 6,
"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 416,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
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"display_name": "Container Orchestration Platforms",
"id": 134,
"rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"slug": "container-orchestration-platforms",
"source": "db"
},
"input_skill": "AKS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Container Orchestration Platforms",
"id": 134,
"rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"slug": "container-orchestration-platforms",
"source": "db"
},
"input_skill": "AKS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "AKS",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Platform",
"skill_nature": "PLATFORM",
"sub_category": "kubernetes_platform",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "AKS is a specific acronym for Azure Kubernetes Service; typical JDs won\u2019t confuse it with other Kubernetes platforms."
},
"context_keywords": {
"context_keywords": [
"Azure",
"Kubernetes",
"containerization",
"Helm",
"kubectl",
"microservices",
"CI/CD",
"DevOps",
"container registry",
"service mesh",
"monitoring",
"scalability",
"load balancing",
"network policies",
"persistent storage"
]
},
"maturity": {
"confidence": 0.92,
"maturity": "well_known",
"reasoning": "AKS appears frequently in cloud/Kubernetes job descriptions and Microsoft actively markets it as a core Azure service, indicating broad enterprise adoption rather than niche use."
},
"skill_id": "aks",
"vendor_license": {
"confidence": 0.9,
"license": "other_open",
"vendor": "Microsoft",
"year_introduced": 2018
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Platforms that schedule, scale, and manage containerized workloads across clusters and environments. AKS belongs here because it is Azure\u0027s managed Kubernetes service used to run and operate container workloads.",
"exemplar_skills": [
"AKS",
"Kubernetes",
"EKS",
"GKE",
"node pools",
"pod scheduling",
"cluster autoscaling"
],
"in_scope": "AKS, Kubernetes clusters, node pools, pod scheduling, service discovery, autoscaling, cluster upgrades, workload placement, managed control planes",
"name": "Container Orchestration Platforms",
"out_of_scope": "Container image build and registry management, Kubernetes network policy design, application deployment scripting, cloud account governance",
"overlap_flags": [
{
"reason": "AKS is an Azure-managed service, so cloud-provider familiarity often overlaps with cluster operations.",
"with_dim_id": "cloud-platforms",
"with_dim_name": null,
"with_role": "Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer"
},
{
"reason": "AKS deployments frequently involve Kubernetes security controls, but this dimension focuses on the platform itself rather than hardening.",
"with_dim_id": "container-and-kubernetes-security",
"with_dim_name": null,
"with_role": "Cybersecurity Engineer"
}
],
"tentative_id": "container-orchestration-platforms"
},
{
"description": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"exemplar_skills": [
"Container Orchestration Platforms"
],
"in_scope": "Skills, tools, and practices that belong under Container Orchestration Platforms for the target role, including items implied by the dimension rationale.",
"name": "Container Orchestration Platforms",
"out_of_scope": "Adjacent clusters explicitly not owned by Container Orchestration Platforms, including unrelated platforms, roles, and skill families per library policy.",
"overlap_flags": [],
"tentative_id": "container-orchestration-platforms"
}
],
"merge_log": [],
"placed": {
"name": "AKS",
"placement_confidence": 0.92,
"primary_dimension": "container-orchestration-platforms",
"reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "aks"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "aks",
"suppress_on_match": []
},
"skill_id": "aks",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.97,
"name": "AKS",
"reasoning": "AKS is a vendor-hosted managed Kubernetes environment with APIs and multi-tenancy, so by the Platform vs Tool rule it is a Platform rather than software you run yourself.",
"skill_id": "aks",
"subtype": "kubernetes_platform",
"type": "Platform"
},
"warnings": [
"stage3_post_filter_dropped_catalog_only_locked_dims:41-\u003e2"
]
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "ACR",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Proficiency in major cloud service provider platforms and their core services.",
"slug": "cloud-platforms",
"source": "db"
},
"input_skill": "ACR",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Proficiency in major cloud service provider platforms and their core services.",
"slug": "cloud-platforms",
"source": "db"
},
"input_skill": "ACR",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
]
}
],
"input_skill": "ACR",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Platform",
"skill_nature": "PLATFORM",
"sub_category": "container_registry_platform",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"acr_azure_container_registry",
"acr_aws_cloudfront_origin_request_control"
],
"reasoning": "ACR is a common acronym; in JDs it may refer to Azure Container Registry or other ACR-related services, not uniquely this platform."
},
"context_keywords": {
"context_keywords": [
"Docker",
"Kubernetes",
"containerization",
"CI/CD",
"image repository",
"artifact management",
"Azure DevOps",
"Helm",
"microservices",
"registry authentication",
"cloud-native",
"DevOps",
"container orchestration",
"scalability",
"versioning"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "well_known",
"reasoning": "Azure Container Registry (ACR) is a standard Azure service commonly listed in cloud/container DevOps job descriptions alongside AKS and Docker; Microsoft continues active support and docs, indicating broad market adoption."
},
"skill_id": "acr",
"vendor_license": {
"confidence": 0.9,
"license": "other_open",
"vendor": "Microsoft",
"year_introduced": 2017
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [
{
"a_dim_id": "d_init_01",
"a_name": "Azure Container Registry",
"a_role": "__skill_focal__",
"b_dim_id": "cloud-platforms",
"b_name": "Cloud Platforms",
"b_role": "__skill_focal__",
"pair_kind": "intra_role",
"reasoning": "Dim A is a specific Azure registry skill: ACR, image tagging/versioning, Helm chart storage, OCI artifacts, geo-replication, and private registry access. Dim B is a broad cloud-platform umbrella covering major provider platforms and core services, with no concrete registry focus. A senior ACR practitioner would not automatically be a senior practitioner in the broader multi-cloud platform cluster. career-track: no, because A is Azure container registry specialization while B is generic cloud-platform proficiency.",
"similarity": 0.6685097753700325
}
],
"locked_dimensions": [
{
"description": "Azure Container Registry (ACR) is the managed registry used to store, version, and distribute container images and related artifacts in Azure-centric delivery flows. It belongs here because ACR is the specific platform skill, not the broader container orchestration or security domains.",
"exemplar_skills": [
"ACR",
"Azure Container Registry",
"container image tagging",
"OCI artifact management",
"geo-replicated container registry",
"private registry authentication"
],
"in_scope": "ACR, Azure Container Registry, container image storage, image tagging and versioning, Helm chart storage, OCI artifacts, geo-replication, repository permissions, image retention policies, private registry access",
"name": "Azure Container Registry",
"out_of_scope": "Kubernetes workload scheduling and cluster management, container runtime hardening, CI/CD pipeline authoring, cloud account governance, which belong to orchestration, security, pipeline, and governance dimensions",
"overlap_flags": [
{
"reason": "ACR is commonly used alongside Kubernetes and other orchestrators, but it is the registry service rather than the orchestration platform itself.",
"with_dim_id": "container-orchestration-platforms",
"with_dim_name": null,
"with_role": "Cloud Architect, DevOps Engineer"
},
{
"reason": "ACR supports image access control and scanning-related workflows, which can overlap with container security concerns.",
"with_dim_id": "container-and-kubernetes-security",
"with_dim_name": null,
"with_role": "Cybersecurity Engineer"
}
],
"tentative_id": "d_init_01"
},
{
"description": "Cloud platforms cover major provider ecosystems and their managed services used to build and operate applications. ACR fits here as an Azure-managed service within the broader cloud platform surface.",
"exemplar_skills": [
"ACR",
"Azure",
"Azure services",
"managed cloud services",
"cloud resource configuration",
"Azure identity integration"
],
"in_scope": "Azure services, Azure Container Registry, compute, storage, networking, identity integrations, managed platform services, cloud resource organization, service configuration",
"name": "Cloud Platforms",
"out_of_scope": "On-premises infrastructure only, generic DevOps tooling, application-level container build logic, which are owned by other platform or delivery dimensions",
"overlap_flags": [
{
"reason": "ACR is a provider-specific Azure service, so it overlaps with the broader cloud provider platform dimension.",
"with_dim_id": "cloud-provider-platforms",
"with_dim_name": null,
"with_role": "Cloud Architect"
}
],
"tentative_id": "cloud-platforms"
},
{
"description": "Proficiency in major cloud service provider platforms and their core services.",
"exemplar_skills": [
"Cloud Platforms"
],
"in_scope": "Skills, tools, and practices that belong under Cloud Platforms for the target role, including items implied by the dimension rationale.",
"name": "Cloud Platforms",
"out_of_scope": "Adjacent clusters explicitly not owned by Cloud Platforms, including unrelated platforms, roles, and skill families per library policy.",
"overlap_flags": [],
"tentative_id": "cloud-platforms"
}
],
"merge_log": [],
"placed": {
"name": "ACR",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 3 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [
"cloud-platforms"
],
"skill_id": "acr"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "acr",
"suppress_on_match": []
},
"skill_id": "acr",
"split_log": [],
"typed": {
"alternatives_considered": [
"Service: ruled out \u2014 although it is a managed capability, the skill name refers to the vendor-hosted registry offering as a whole, which the rules classify as Platform.",
"Tool: ruled out \u2014 it is not self-hosted software operated locally by the user."
],
"confidence": 0.88,
"name": "ACR",
"reasoning": "By the Platform vs Tool rule, ACR (Azure Container Registry) is a hosted, multi-tenant Azure service with APIs rather than software you run yourself, so it fits Platform.",
"skill_id": "acr",
"subtype": "container_registry_platform",
"type": "Platform"
},
"warnings": [
"stage3_post_filter_dropped_catalog_only_locked_dims:42-\u003e3"
]
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"FastAPI",
"Databricks",
"Unity Catalog",
"Agentic workflows",
"Vector DB",
"Hybrid Search",
"Prompt engineering",
"AWS Bedrock",
"DSPy",
"Redshift",
"RDS",
"S3",
"Vector Search",
"Azure DevOps",
"Scrum",
"DevOps",
"IAM",
"Monitoring",
"Load Balancing",
"ECR",
"AKS",
"ACR"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "AI Engineer",
"id": 13,
"rationale": "The primary skills indicate a strong emphasis on AI technologies and cloud-based solutions.",
"role_archetype": "Engineering role focused on developing AI solutions and infrastructure.",
"slug": "ai-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "FastAPI",
"tag": "new"
},
{
"skill": "Django",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "Databricks",
"tag": "new"
},
{
"skill": "Unity Catalog",
"tag": "new"
},
{
"skill": "Terraform",
"tag": "in_db"
},
{
"skill": "CloudFormation",
"tag": "in_db"
},
{
"skill": "React",
"tag": "in_db"
},
{
"skill": "Next.js",
"tag": "in_db"
},
{
"skill": "Tailwind CSS",
"tag": "in_db"
},
{
"skill": "RAG",
"tag": "in_db"
},
{
"skill": "Agentic workflows",
"tag": "new"
},
{
"skill": "Vector DB",
"tag": "new"
},
{
"skill": "Hybrid Search",
"tag": "new"
},
{
"skill": "Prompt engineering",
"tag": "new"
},
{
"skill": "OpenAI",
"tag": "in_db"
},
{
"skill": "Anthropic",
"tag": "in_db"
},
{
"skill": "AWS Bedrock",
"tag": "new"
},
{
"skill": "LangChain",
"tag": "in_db"
},
{
"skill": "DSPy",
"tag": "new"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "Redshift",
"tag": "new"
},
{
"skill": "RDS",
"tag": "new"
},
{
"skill": "S3",
"tag": "new"
},
{
"skill": "Redis",
"tag": "in_db"
},
{
"skill": "Vector Search",
"tag": "new"
},
{
"skill": "Git",
"tag": "in_db"
},
{
"skill": "Azure DevOps",
"tag": "new"
},
{
"skill": "Agile",
"tag": "in_db"
},
{
"skill": "Scrum",
"tag": "new"
},
{
"skill": "DevOps",
"tag": "new"
},
{
"skill": "CI/CD",
"tag": "in_db"
},
{
"skill": "IAM",
"tag": "new"
},
{
"skill": "Monitoring",
"tag": "new"
},
{
"skill": "Load Balancing",
"tag": "new"
},
{
"skill": "Autoscaling",
"tag": "in_db"
},
{
"skill": "ECR",
"tag": "new"
},
{
"skill": "AKS",
"tag": "new"
},
{
"skill": "ACR",
"tag": "new"
}
],
"persistence": {
"items": [
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Core server-side languages used to implement backend business logic, integrations, and service internals. This is the primary coding surface for the role across application layers.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
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"skill_dimension_saved": true,
"skill_id": 1215,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD Pipeline Platforms",
"id": 150,
"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"dimension_id": 150,
"input_skill": "DevOps",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
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"role_archetype": null,
"slug": "devops-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 1216,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code",
"id": 132,
"rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
"slug": "infrastructure-as-code",
"source": "db"
},
"dimension_id": 132,
"input_skill": "DevOps",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1216,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Release Patterns",
"id": 140,
"rationale": "Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.",
"slug": "deployment-and-release-patterns",
"source": "db"
},
"dimension_id": 140,
"input_skill": "DevOps",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
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}
],
"skill_dimension_saved": true,
"skill_id": 1216,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "IAM",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1217,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Observability and Incident Triage",
"id": 155,
"rationale": "Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.",
"slug": "observability-and-incident-triage",
"source": "db"
},
"dimension_id": 155,
"input_skill": "Monitoring",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1218,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Release Patterns",
"id": 140,
"rationale": "Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.",
"slug": "deployment-and-release-patterns",
"source": "db"
},
"dimension_id": 140,
"input_skill": "Load Balancing",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
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}
],
"skill_dimension_saved": true,
"skill_id": 1219,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 13,
"dimension": {
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"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Proficiency in major cloud service provider platforms and their core services.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "ECR",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
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},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1220,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 13,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Container Orchestration Platforms",
"id": 134,
"rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"slug": "container-orchestration-platforms",
"source": "db"
},
"dimension_id": 134,
"input_skill": "AKS",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
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"slug": "cloud-architect",
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},
{
"display_name": "DevOps Engineer",
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"slug": "devops-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 1221,
"skill_tag": "in_db",
"skipped_reason": null
},
{
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"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "ACR",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
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},
{
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"display_name": "Cloud Platforms",
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"rationale": "Proficiency in major cloud service provider platforms and their core services.",
"slug": "cloud-platforms",
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},
"dimension_id": 20,
"input_skill": "ACR",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Backend Engineer",
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"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
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},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
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},
{
"display_name": "DevOps Engineer",
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"role_archetype": null,
"slug": "devops-engineer",
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},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1222,
"skill_tag": "in_db",
"skipped_reason": null
}
],
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},
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}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.