Pipeline run
66e204bd-d3bb-4eba-ad86-abe9d08e225d
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
- Build and ship GenAI features and AI-powered products to enterprise customers. - Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering. - Develop RAG pipelines on top …
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
LLM / GenAI Engineer
domain · AI / ML CASE DOMAINslug: llm-genai-engineer · id: 151 · source: db
Domain=AI / ML; The JD is centered on building GenAI products, RAG, prompt engineering, model serving, and fine-tuning/deploying small models, which best matches an LLM/GenAI engineering role.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Senior AI Engineer About the role: We are hiring a Senior AI Engineer to join our applied AI team. Responsibilities: - Build and ship GenAI features and AI-powered products to enterprise customers. - Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering. - Develop RAG pipelines on top of vector databases (Pinecone, pgvector). - Use Copilot, Cursor, and Claude for day-to-day development. - Fine-tune and deploy small models for product-specific tasks. Required Skills: LangChain, OpenAI SDK, Hugging Face Transformers, Python, FastAPI, PyTorch, RAG, LLM, embeddings Docker, Kubernetes, AWS, PostgreSQL
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
- 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: AI Engineer, ML Engineer, MLOps 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) |
Aliases — catalog
- OpenAI API (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Llm Api Service
- Vendor
- OpenAI
- License
- other_open
- Year introduced
- 2020
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Strong JD signal: OpenAI API is now commonly listed in AI/ML and full-stack roles, and OpenAI’s own platform docs and ecosystem integrations show broad production adoption.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 1006
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Provider APIs Catalog dimension db id 195
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Provider APIs
llm-provider-apis
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security 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, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | 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) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- FastAPI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Sebastián Ramírez
- License
- mit
- Year introduced
- 2018
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: FastAPI appears in many Python backend job postings and has strong GitHub adoption; it’s now a common choice for API development alongside Flask/Django rather than a niche tool.
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)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
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) |
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- PyTorch (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- Meta
- License
- bsd
- Year introduced
- 2016
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PyTorch appears in a large volume of ML/AI job descriptions and is a standard framework in research and production, alongside TensorFlow and CUDA ecosystems.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Model Fine-Tuning & Adaptation Catalog dimension db id 212
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | 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) |
Aliases — catalog
- LLMs (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Large Language Models
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LLMs are increasingly listed in job descriptions for AI/ML and product roles, and major vendors (OpenAI, Anthropic, Google) are shipping APIs and platforms, but they are not yet universal across engineering hiring.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 903
- 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
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- Embeddings (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Vector Representation
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Embeddings are a standard ML concept and appear widely in JDs for search, recommendation, and LLM/RAG roles; major vendors like OpenAI, Cohere, and AWS expose embedding APIs, signaling broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 905
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Systems Programming Catalog dimension db id 166
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Systems Programming
d_init_02
|
✓ | — | 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
- 63
- 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 Cloud Platforms Catalog dimension db id 418
Library dimension (catalog)
Roles linked in library: Ruby Backend Developer
-
Deployment and Runtime Configuration Catalog dimension db id 13
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer
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 Cloud Platforms
deployment-and-cloud-platforms
|
✓ | — | 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
-
Kubernetes for ML Workloads Catalog dimension db id 47
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps 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) |
|
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
|
✓ | — | 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: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
-
Cloud Platforms for AI Deployment Catalog dimension db id 211
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Cloud Provider Platforms Catalog dimension db id 131
Library dimension (catalog)
Roles linked in library: Cloud Architect, Cloud Security Engineer
-
Cloud Security Posture Tools Catalog dimension db id 64
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer, Cyber Security Engineer
-
Vendor Product Families Catalog dimension db id 477
Library dimension (catalog)
Roles linked in library: Engineering Manager
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 Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | 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) |
|
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- PostgreSQL (CANONICAL) primary
- PG 13 (VERSION)
- PG 14 (VERSION)
- PG 15 (VERSION)
- PG 16 (VERSION)
- PostgreSQL 13 (VERSION)
- PostgreSQL 14 (VERSION)
- PostgreSQL 15 (VERSION)
- PostgreSQL 16 (VERSION)
- Postgres 13 (VERSION)
- Postgres 14 (VERSION)
- Postgres 15 (VERSION)
- Postgres 16 (VERSION)
- pg10 (VERSION)
- pg11 (VERSION)
- pg12 (VERSION)
- pg13 (VERSION)
- pg14 (VERSION)
- pg15 (VERSION)
- pg16 (VERSION)
- postgres (VERSION)
- postgresql 10 (VERSION)
- postgresql 11 (VERSION)
- postgresql 12 (VERSION)
- postgresql 13 (VERSION)
- postgresql 14 (VERSION)
- postgresql 15 (VERSION)
- postgresql 16 (VERSION)
- postgresql-16 (VERSION)
- postgresql10 (VERSION)
- postgresql11 (VERSION)
- postgresql12 (VERSION)
- postgresql13 (VERSION)
- postgresql14 (VERSION)
- postgresql15 (VERSION)
- postgresql16 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- PostgreSQL Global Development Group
- License
- other_open
- Year introduced
- 1996
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Data Modeling Catalog dimension db id 216
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
-
Relational Database Usage Catalog dimension db id 371
Library dimension (catalog)
Roles linked in library: Go Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Data Modeling
relational-data-modeling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Usage
relational-database-usage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Pinecone (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Vector Database Platform
- Vendor
- Pinecone
- License
- unknown
- Year introduced
- 2021
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Pinecone appears in a growing number of AI/vector-search job postings and vendor docs, but it is still far from universal compared with PostgreSQL or AWS.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 177
- 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: AI Engineer, ML Engineer, MLOps Engineer
-
Vector Databases Catalog dimension db id 198
Library dimension (catalog)
Roles linked in library: AI 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) |
|
Vector Databases
vector-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- pgvector (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Database Extension Library
- Vendor
- ZomboDB
- License
- mit
- Year introduced
- 2021
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Appears in growing numbers of JDs for AI search/RAG roles, but remains a PostgreSQL extension rather than a universal database skill; GitHub adoption is rising yet still far below core DB tech.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 972
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Vector Databases Catalog dimension db id 198
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Vector Databases
vector-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Development Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Aliases — catalog
- Cursor (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Ai Code Editor
- Vendor
- Cursor
- License
- unknown
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Cursor is increasingly appearing in developer job posts and AI-tooling stacks, but it is not yet a universal hiring staple like VS Code or GitHub Copilot.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 1195
- 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)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- AI Models
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
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 |
|---|---|---|---|---|---|---|
| LangChain | in_db |
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| OpenAI SDK | new |
LLM Provider APIs
llm-provider-apis
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | 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) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| FastAPI | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| FastAPI | in_db |
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PyTorch | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PyTorch | in_db |
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | 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) | |
| LLM | new |
React Frontend Development
d_init_01
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| embeddings | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| embeddings | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| embeddings | in_db |
Systems Programming
d_init_02
|
✓ | — | 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 Cloud Platforms
deployment-and-cloud-platforms
|
✓ | — | 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) | |
| Kubernetes | in_db |
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
|
✓ | — | 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 Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | 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) | |
| AWS | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PostgreSQL | in_db |
Relational Data Modeling
relational-data-modeling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PostgreSQL | in_db |
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PostgreSQL | in_db |
Relational Database Usage
relational-database-usage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Pinecone | in_db |
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Pinecone | in_db |
Vector Databases
vector-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| pgvector | in_db |
Vector Databases
vector-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Cursor | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Hugging Face Transformers | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Copilot | type=Development Tools subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | Claude | type=AI Models subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| dimension_skill_link_proposed | OpenAI SDK ↔ LLM Provider APIs | |
| dimension_skill_link_proposed | LLM ↔ React Frontend Development |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"ai_kras": [
"Build and ship GenAI features and AI-powered products to enterprise customers.",
"Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.",
"Develop RAG pipelines on top of vector databases (Pinecone, pgvector).",
"Use Copilot, Cursor, and Claude for day-to-day development.",
"Fine-tune and deploy small models for product-specific tasks."
],
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
"education": [],
"experience": null,
"job_locations": [],
"role": "Senior AI Engineer",
"role_aliases": [
"AI Engineer",
"Machine Learning Engineer",
"Senior AI Developer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Build and ship GenAI features",
"last_5_words": "for product-specific tasks."
},
"text": "- Build and ship GenAI features and AI-powered products to enterprise customers.\n- Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.\n- Develop RAG pipelines on top of vector databases (Pinecone, pgvector).\n- Use Copilot, Cursor, and Claude for day-to-day development.\n- Fine-tune and deploy small models for product-specific tasks.",
"word_count": 51
},
{
"bullet_count": 0,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "LangChain, OpenAI SDK, Hugging",
"last_5_words": "AWS, PostgreSQL"
},
"text": "LangChain, OpenAI SDK, Hugging Face Transformers, Python, FastAPI, PyTorch, RAG, LLM, embeddings\nDocker, Kubernetes, AWS, PostgreSQL",
"word_count": 24
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "LangChain"
},
{
"is_primary": true,
"skill_name": "OpenAI SDK"
},
{
"is_primary": true,
"skill_name": "Hugging Face Transformers"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "FastAPI"
},
{
"is_primary": true,
"skill_name": "PyTorch"
},
{
"is_primary": true,
"skill_name": "RAG"
},
{
"is_primary": true,
"skill_name": "LLM"
},
{
"is_primary": true,
"skill_name": "embeddings"
},
{
"is_primary": true,
"skill_name": "Docker"
},
{
"is_primary": true,
"skill_name": "Kubernetes"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "PostgreSQL"
},
{
"is_primary": true,
"skill_name": "Pinecone"
},
{
"is_primary": true,
"skill_name": "pgvector"
},
{
"is_primary": true,
"skill_name": "Copilot"
},
{
"is_primary": true,
"skill_name": "Cursor"
},
{
"is_primary": true,
"skill_name": "Claude"
}
],
"jd_role": {
"display_name": "Senior AI Engineer",
"rationale": null,
"role_aliases": [
"AI Engineer",
"Machine Learning Engineer",
"Senior AI Developer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"ai_kras": [
"Build and ship GenAI features and AI-powered products to enterprise customers.",
"Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.",
"Develop RAG pipelines on top of vector databases (Pinecone, pgvector).",
"Use Copilot, Cursor, and Claude for day-to-day development.",
"Fine-tune and deploy small models for product-specific tasks."
],
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
"education": [],
"experience": null,
"job_locations": [],
"role": "Senior AI Engineer",
"role_aliases": [
"AI Engineer",
"Machine Learning Engineer",
"Senior AI Developer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Build and ship GenAI features",
"last_5_words": "for product-specific tasks."
},
"text": "- Build and ship GenAI features and AI-powered products to enterprise customers.\n- Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.\n- Develop RAG pipelines on top of vector databases (Pinecone, pgvector).\n- Use Copilot, Cursor, and Claude for day-to-day development.\n- Fine-tune and deploy small models for product-specific tasks.",
"word_count": 51
},
{
"bullet_count": 0,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "LangChain, OpenAI SDK, Hugging",
"last_5_words": "AWS, PostgreSQL"
},
"text": "LangChain, OpenAI SDK, Hugging Face Transformers, Python, FastAPI, PyTorch, RAG, LLM, embeddings\nDocker, Kubernetes, AWS, PostgreSQL",
"word_count": 24
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "66e204bd-d3bb-4eba-ad86-abe9d08e225d",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 1.0,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 1.0,
"slug": "ai-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "AI Engineer",
"kra_matches": [
{
"kra_text": "Translates product requirements into AI-powered features by integrating large language models like GPT-4, Claude, or Gemini into application workflows via API.",
"sentence": "Build and ship GenAI features and AI-powered products to enterprise customers.",
"similarity": 0.5856
},
{
"kra_text": "Optimizes AI pipeline efficiency by tuning model selection, context window usage, prompt caching, and batching strategies to reduce cost and latency.",
"sentence": "Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.",
"similarity": 0.5516
},
{
"kra_text": "Optimizes AI pipeline efficiency by tuning model selection, context window usage, prompt caching, and batching strategies to reduce cost and latency.",
"sentence": "Fine-tune and deploy small models for product-specific tasks.",
"similarity": 0.4643
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.5338,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
"sentence": "Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.",
"similarity": 0.5385
},
{
"kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
"sentence": "Build and ship GenAI features and AI-powered products to enterprise customers.",
"similarity": 0.5161
},
{
"kra_text": "Supports offline experimentation, hyperparameter tuning, and online A/B experiments to improve model quality and investigate production performance issues.",
"sentence": "Fine-tune and deploy small models for product-specific tasks.",
"similarity": 0.5103
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.5216,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Supports ML platform incidents by diagnosing model serving failures, feature store pipeline breaks, and training environment configuration issues.",
"sentence": "Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.",
"similarity": 0.5719
},
{
"kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
"sentence": "Fine-tune and deploy small models for product-specific tasks.",
"similarity": 0.4527
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Build and ship GenAI features and AI-powered products to enterprise customers.",
"similarity": 0.4351
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4866,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "AI Compliance Officer",
"kra_matches": [
{
"kra_text": "Manages AI deployment approval workflows, periodic reassessment calendars, and conditional authorization records for production AI systems.",
"sentence": "Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.",
"similarity": 0.4979
},
{
"kra_text": "Manages AI deployment approval workflows, periodic reassessment calendars, and conditional authorization records for production AI systems.",
"sentence": "Build and ship GenAI features and AI-powered products to enterprise customers.",
"similarity": 0.4804
},
{
"kra_text": "Reviews AI use cases and model deployments against applicable regulations, internal ethics policies, and governance guidelines prior to production approval.",
"sentence": "Fine-tune and deploy small models for product-specific tasks.",
"similarity": 0.4058
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 12,
"score": 0.4613,
"slug": "ai-compliance-officer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
"sentence": "Build and ship GenAI features and AI-powered products to enterprise customers.",
"similarity": 0.4876
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Develop RAG pipelines on top of vector databases (Pinecone, pgvector).",
"similarity": 0.4763
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Fine-tune and deploy small models for product-specific tasks.",
"similarity": 0.4126
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4589,
"slug": "full-stack-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 7,
"matched_skills": [
"AWS",
"Embeddings",
"Kubernetes",
"LangChain",
"Pinecone",
"PyTorch",
"Python"
],
"role_id": 16,
"score": 0.3889,
"slug": "ml-ops-engineer",
"total_count": 18
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 7,
"matched_skills": [
"AWS",
"Embeddings",
"Kubernetes",
"LangChain",
"Pinecone",
"PyTorch",
"Python"
],
"role_id": 3,
"score": 0.3889,
"slug": "ml-engineer",
"total_count": 18
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 5,
"matched_skills": [
"AWS",
"Docker",
"FastAPI",
"PostgreSQL",
"Python"
],
"role_id": 1,
"score": 0.2778,
"slug": "backend-engineer",
"total_count": 18
},
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": 5,
"matched_skills": [
"AWS",
"LangChain",
"Pinecone",
"PyTorch",
"pgvector"
],
"role_id": 13,
"score": 0.2778,
"slug": "ai-engineer",
"total_count": 18
},
{
"display_name": "Fullstack Developer",
"kra_matches": null,
"matched_count": 4,
"matched_skills": [
"AWS",
"FastAPI",
"PostgreSQL",
"Python"
],
"role_id": 15,
"score": 0.2222,
"slug": "full-stack-engineer",
"total_count": 18
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 151,
"score": 0.97,
"slug": "llm-genai-engineer",
"total_count": null
},
"confidence": 0.97,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"GenAI Product Engineering",
"RAG Pipeline Development",
"Model Serving and Evaluation",
"Prompt Engineering",
"Model Fine-tuning and Deployment",
"Enterprise AI Solutions"
],
"matched_kras": [
"Build and ship GenAI features and AI-powered products",
"Collaborate on model serving, evaluation, and prompt engineering",
"Develop RAG pipelines on top of vector databases",
"Fine-tune and deploy small models for product-specific tasks"
],
"matched_skills": [
"GenAI",
"AI-powered products",
"model serving",
"evaluation",
"prompt engineering",
"RAG",
"Pinecone",
"pgvector",
"Copilot",
"Cursor",
"Claude",
"LangChain",
"OpenAI SDK",
"Hugging Face Transformers",
"Python"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=AI / ML; The JD is centered on building GenAI products, RAG, prompt engineering, model serving, and fine-tuning/deploying small models, which best matches an LLM/GenAI engineering role.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 18,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 2023,
"r_and_r_preview": "- Build and ship GenAI features and AI-powered products to enterprise customers.\n- Collaborate with the AI/ML team on model serving, evaluation, and prompt engineering.\n- Develop RAG pipelines on top ",
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 25809,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "OpenAI SDK",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 25810,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Hugging Face Transformers",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 25811,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "LLM",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 25812,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Copilot",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 25813,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
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}
}
API 2 — extract-details
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{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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"alias_persisted": false,
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"existing_alias_text": "Docker",
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"alias_persisted": false,
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"alias_persisted": false,
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"existing_alias_text": "AWS",
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"matched_canonical": {
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},
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"alias_persisted": false,
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"alias_persisted": false,
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"existing_alias_text": "Pinecone",
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"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1882,
"existing_alias_text": "pgvector",
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"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2535,
"existing_alias_text": "Cursor",
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"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"input_skill": "Pinecone",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vector Databases",
"id": 198,
"rationale": "Specialized storage and indexing systems used to persist embeddings and support similarity search. This is a distinct vendor-family cluster because AI features often depend on a concrete vector store choice and its operational behavior.",
"slug": "vector-databases",
"source": "db"
},
"input_skill": "Pinecone",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
}
],
"input_skill": "Pinecone",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "pgvector",
"alias_type": "CANONICAL",
"id": 1882,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 7,
"display_name": "pgvector",
"id": 1246,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LIBRARY",
"slug": "pgvector",
"sub_category_id": 972,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vector Databases",
"id": 198,
"rationale": "Specialized storage and indexing systems used to persist embeddings and support similarity search. This is a distinct vendor-family cluster because AI features often depend on a concrete vector store choice and its operational behavior.",
"slug": "vector-databases",
"source": "db"
},
"input_skill": "pgvector",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
}
],
"input_skill": "pgvector",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Copilot",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Development Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "SHORT_LIVED",
"version_strategy": "VERSIONED",
"volatility": "FAST"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "copilot",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Cursor",
"alias_type": "CANONICAL",
"id": 2535,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Cursor",
"id": 1589,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "cursor",
"sub_category_id": 1195,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"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": "Cursor",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Cursor",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Claude",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "AI Models",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "SHORT_LIVED",
"version_strategy": "VERSIONED",
"volatility": "FAST"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "claude",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Hugging Face Transformers",
"Copilot",
"Claude"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"id": 151,
"rationale": "Domain=AI / ML; The JD is centered on building GenAI products, RAG, prompt engineering, model serving, and fine-tuning/deploying small models, which best matches an LLM/GenAI engineering role.",
"role_archetype": null,
"slug": "llm-genai-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "LangChain",
"tag": "in_db"
},
{
"skill": "OpenAI SDK",
"tag": "in_db"
},
{
"skill": "Hugging Face Transformers",
"tag": "new"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "FastAPI",
"tag": "in_db"
},
{
"skill": "PyTorch",
"tag": "in_db"
},
{
"skill": "RAG",
"tag": "in_db"
},
{
"skill": "LLM",
"tag": "in_db"
},
{
"skill": "embeddings",
"tag": "in_db"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "PostgreSQL",
"tag": "in_db"
},
{
"skill": "Pinecone",
"tag": "in_db"
},
{
"skill": "pgvector",
"tag": "in_db"
},
{
"skill": "Copilot",
"tag": "new"
},
{
"skill": "Cursor",
"tag": "in_db"
},
{
"skill": "Claude",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"dimension_id": 49,
"input_skill": "LangChain",
"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": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 240,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Provider APIs",
"id": 195,
"rationale": "Direct integration with hosted model APIs used to generate, classify, extract, and transform content. This is the primary vendor surface for shipping AI features because it determines model choice, request shape, streaming, tool use, and cost/latency tradeoffs.",
"slug": "llm-provider-apis",
"source": "db"
},
"dimension_id": 195,
"input_skill": "OpenAI SDK",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"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": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"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 Developer",
"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": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"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": "Cyber Security 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": 151,
"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": 151,
"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"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"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"
},
"dimension_id": 97,
"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": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"dimension_id": 290,
"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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"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": "FastAPI",
"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": [],
"skill_dimension_saved": true,
"skill_id": 1201,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Web Application Frameworks",
"id": 2,
"rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
"slug": "web-application-frameworks",
"source": "db"
},
"dimension_id": 2,
"input_skill": "FastAPI",
"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 Developer",
"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": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1201,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ML Frameworks and Libraries",
"id": 40,
"rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
"slug": "ml-frameworks-and-libraries",
"source": "db"
},
"dimension_id": 40,
"input_skill": "PyTorch",
"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"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 195,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Fine-Tuning \u0026 Adaptation",
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"rationale": "Techniques and libraries for adapting pre-trained language models to specific tasks or domains.",
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