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Pipeline run

27dfab9e-23a4-4b41-86f7-9a3616035d52

Pipeline LLM cost (USD)
API 1: $0.0097 API 2: $0.0004 API 3: $0.0000 Total: $0.0101

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · AI/ML Engineering
Build and optimize GPU-accelerated RAG and LLM agent services, taking them from design and testing to scalable multi-node, multi-cloud deployment. Also tune accuracy/metrics, integrate with other products, and review code/design in a collaborative delivery cycle.
"Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow."
Tech stack maturity
Modern Cloud Native
The skill set combines cloud-native deployment and orchestration tools like Docker, Kubernetes, Helm, CI/CD, and microservices with modern GenAI frameworks such as LangChain and LlamaIndex, indicating a contemporary cloud-native AI engineering stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2): LangChain, LlamaIndex, Modal
Models / concepts (×3): RAG, embeddings, LLM, agentic, AI agent, function calling, guardrails, AI, ML, Machine Learning, Deep Learning, Artificial Intelligence
Evidence — skills matched in JD (23)
Python Retrieval Augmented Generation Microservices Docker Helm Kubernetes CI/CD Large Language Models LLM function calling Information Retrieval Vector Databases Embedding models Rerank models Git LangChain LlamaIndex Machine Learning Deep Learning CUDA cuDNN TensorRT Gerrit GitLab
Skill cluster (6 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Containerization and Image Builds
Docker
Deployment and Runtime Configuration
Helm
Kubernetes for ML Workloads
Kubernetes
Python Programming
Python
Cross-cutting / unaligned
Retrieval Augmented Generation Microservices CI/CD Large Language Models LLM function calling Information Retrieval Vector Databases Embedding models Rerank models Git LangChain LlamaIndex Deep Learning CUDA cuDNN TensorRT Gerrit GitLab
Show KRA description ↓
• Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow. Build a scalable microservice based architecture deployable on multi-node, multi-cloud environment • Designing, implementing and testing domain specific agents and workflows and a framework which can support multi-turn, multi-modal, multi-user conversations with a LLM driven agents. • Develop knowledge discovery, and reasoning capabilities including but not limited to disambiguation, clarification, and anticipation for dialogue systems • Analyze RAG and conversational AI agent end to end accuracy and limitations and recommend the next course of action & Improvements. • Characterize performance and quality metrics across platforms for various AI and system components • Collaborate with various teams on new product features and improvements of existing products. Customize and integrate the conversational AI framework with other NVIDIA products • Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment. • Bachelor's degree or Master’s degree (or equivalent experience) in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math • 5+ years of experience • Excellent programming skills in Python • Hands-on experience of working with Retrieval Augmented Generation based applications • Knowhow of Large Language model applications, agentic workflows, LLM guardrails • Understanding of scalable deployment of LLM driven RAG and Agent applications in production environment • Familiarity with microservices, Docker, helm, kubernetes etc. • Experience of working on end to end Software lifecycle, release packaging & CI/CD pipeline • Hands-on experience on conversational AI Technologies like Large Language Model(LLM), LLM function calling, Information Retrieval, Vector Databases, Embedding and Rerank models, autonomous agents etc. • General background around version control and code review tools like Git, Gerrit, Gitlab. • Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic environment • Strong fundamentals in Programming, optimizations and Software design • Experience of working with open source frameworks like LangChain, LlamaIndex for building LLM driven applications • Strong knowledge of ML/DL techniques, algorithms and tools with exposure to Language Models • Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT • Background with deploying machine learning models on data center, cloud, and embedded systems

Signals

Skill ml-engineer
0.33
Alias
KRA flutter-developer
0.56

Post-classification

Centroidupdated · n=7
Alias collision log
New-role queue
New skills captured11
New KRA capturedyes

Captured for admin review

Retrieval Augmented Generation primary LLM / GenAI Engineer pending
Large Language Models primary LLM / GenAI Engineer pending
LLM function calling primary LLM / GenAI Engineer pending
Information Retrieval primary LLM / GenAI Engineer pending
Vector Databases primary LLM / GenAI Engineer pending
Embedding models primary LLM / GenAI Engineer pending
Rerank models primary LLM / GenAI Engineer pending
Gerrit LLM / GenAI Engineer pending
Deep Learning primary LLM / GenAI Engineer pending
CUDA primary LLM / GenAI Engineer pending
cuDNN primary LLM / GenAI Engineer pending
R&R fragment (sim 0.00) LLM / GenAI Engineer pending

• Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow. Build a scalable microservice based architecture deployable on multi-node, multi-cloud enviro…

Status: completed Created: 2026-05-27T16:04:41.636908Z Updated: 2026-06-08T22:50:55.811275Z API 3 duration: 49219 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

LLM / GenAI Engineer

domain · AI / ML CASE DOMAIN

slug: llm-genai-engineer · id: 151 · source: db

Domain=AI / ML; The JD is centered on building and optimizing RAG, LLM-driven agents, multimodal conversational workflows, and GenAI application deployment, which aligns most strongly with an LLM/GenAI Engineer.

Matched skills

GPU acceleratedRetrieval Augmented Generation(RAG)microservice based architecturemulti-node, multi-cloud environmentLLM driven agentslarge language modelLLM function callingInformation RetrievalVector DatabasesEmbedding and Rerank modelsLangChainLlamaIndexDockerhelmkubernetes

Matched dimensions

RAG and agentic AI application engineeringScalable distributed GenAI systemsConversational AI workflow designLLM application deployment and operationsPerformance optimization and quality analysisSoftware lifecycle and CI/CD integrationCross-functional product integration

Matched KRAs

Architect, implement and optimize GPU accelerated scalable RAG workflowBuild a scalable microservice based architectureDesigning, implementing and testing domain specific agents and workflowssupport multi-turn, multi-modal, multi-user conversationsDevelop knowledge discovery, and reasoning capabilitiesAnalyze RAG and conversational AI agent end to end accuracyCharacterize performance and quality metrics across platformsCustomize and integrate the conversational AI framework

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
1
Skipped

Job description

NVIDIA's technology is at the heart of the AI revolution, touching people across the planet by powering everything from self-driving cars, robotics, and intelligent assistants. Come join the team and see how you can make a lasting impact on the world! We're looking to grow our company, and build our teams with the smartest people in the world. Join us at the forefront of technological advancement. NVIDIA is looking for a System Software Engineer to develop tools for building powerful, flexible, multi-modal AI agents driven by Large Language Models(LLM) & improve the experience of millions of customers. If you're creative & passionate about solving real world conversational AI problems, come join us.

What You’ll Be Doing

• Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow. Build a scalable microservice based architecture deployable on multi-node, multi-cloud environment
• Designing, implementing and testing domain specific agents and workflows and a framework which can support multi-turn, multi-modal, multi-user conversations with a LLM driven agents.
• Develop knowledge discovery, and reasoning capabilities including but not limited to disambiguation, clarification, and anticipation for dialogue systems
• Analyze RAG and conversational AI agent end to end accuracy and limitations and recommend the next course of action & Improvements.
• Characterize performance and quality metrics across platforms for various AI and system components
• Collaborate with various teams on new product features and improvements of existing products. Customize and integrate the conversational AI framework with other NVIDIA products
• Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.


What We Need To See

• Bachelor's degree or Master’s degree (or equivalent experience) in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math
• 5+ years of experience
• Excellent programming skills in Python
• Hands-on experience of working with Retrieval Augmented Generation based applications
• Knowhow of Large Language model applications, agentic workflows, LLM guardrails
• Understanding of scalable deployment of LLM driven RAG and Agent applications in production environment
• Familiarity with microservices, Docker, helm, kubernetes etc.
• Experience of working on end to end Software lifecycle, release packaging & CI/CD pipeline
• Hands-on experience on conversational AI Technologies like Large Language Model(LLM), LLM function calling, Information Retrieval, Vector Databases, Embedding and Rerank models, autonomous agents etc.
• General background around version control and code review tools like Git, Gerrit, Gitlab.
• Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic environment


Ways To Stand Out From The Crowd

• Strong fundamentals in Programming, optimizations and Software design
• Experience of working with open source frameworks like LangChain, LlamaIndex for building LLM driven applications
• Strong knowledge of ML/DL techniques, algorithms and tools with exposure to Language Models
• Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT
• Background with deploying machine learning models on data center, cloud, and embedded systems


NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

JR1987967

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

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)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

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)
Retrieval Augmented Generation Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: retrieval-augmented generation id=1235 · retrieval-augmented-generation

Aliases — catalog

  • retrieval-augmented generation (CANONICAL) primary

Context tags (catalog)

contextual retrieval data augmentation document ranking embedding techniques fine-tuning information retrieval knowledge base multi-hop reasoning natural language understanding query expansion retrieval pipelines retrieval strategies retrieval-augmented models semantic search transformer models

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Rag Architecture
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: RAG is increasingly listed in AI/ML job descriptions and vendor docs, with strong GitHub activity around LangChain/LlamaIndex, but it is not yet a universal hiring staple.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
930
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • RAG Architectures Catalog dimension db id 197

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
RAG Architectures
rag-architectures
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Microservices Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: microservices id=41 · microservices

Aliases — catalog

  • microservices (CANONICAL) primary

Context tags (catalog)

API Gateway API gateway CQRS DevOps Docker Kubernetes REST API RESTful services Saga pattern Spring Boot circuit breaker containerization decentralized distributed tracing domain-driven design event sourcing event-driven event-driven architecture gRPC load balancing message broker microservices patterns monitoring scalability service discovery service mesh

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Distributed System Architecture
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Microservices is a common architecture in job descriptions across backend/cloud roles, and major vendors like AWS, Google Cloud, and Kubernetes ecosystems provide first-class support and reference patterns.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Microservices and Distributed Systems Catalog dimension db id 9

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Node.js Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Microservices and Distributed Systems
microservices-and-distributed-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Docker id=61 · docker

Aliases — catalog

  • Docker (CANONICAL) primary

Context tags (catalog)

CI/CD Compose DevOps Docker Compose Docker Swarm Dockerfile Kubernetes build pipeline container container lifecycle container orchestration container registry container security containerization containers image image registry images immutable infrastructure lightweight virtualization microservices networking orchestration port mapping registry scalability service discovery swarm volume volume management

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)
Helm Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Helm id=63 · helm

Aliases — catalog

  • Helm (CANONICAL) primary

Context tags (catalog)

CI/CD ConfigMap Helm Hub Helm plugins Helmfile Kubernetes OCI registry Secret Tiller YAML chart repository charts custom charts dependencies dependency management helmfile kubectl namespace package management release release management repositories templates values.yaml

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Package Manager Tool
Vendor
CNCF
License
apache_2
Year introduced
2015
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Helm is widely listed in Kubernetes/platform engineering JDs and is the de facto package manager for Kubernetes charts, with strong GitHub adoption and vendor docs from CNCF ecosystem.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
67
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

  • 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
Container Orchestration Platforms
container-orchestration-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)
Kubernetes Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kubernetes id=726 · kubernetes

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)

CI/CD Cluster Autoscaler ConfigMap DaemonSet Deployment Docker Grafana Helm Ingress Istio K8s Kubelet Namespace Pod Prometheus RBAC Secret Service StatefulSet containerization deployment etcd kubectl load balancing microservices namespace orchestration persistent storage scalability service mesh

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)
CI/CD Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=1190 · ci-cd

Aliases — catalog

  • CI/CD (CANONICAL)

Context tags (catalog)

Ansible CircleCI Docker GitLab CI Jenkins Kubernetes Terraform Travis CI automated testing build automation continuous deployment continuous integration deployment pipelines monitoring version control

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)
Large Language Models Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
LLM function calling Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Information Retrieval Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Vector Databases Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Embedding models Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Rerank models Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Git Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Git id=1002 · git

Aliases — catalog

  • Git (CANONICAL)

Context tags (catalog)

CI/CD GitHub GitLab branching checkout clone commit fork merging pull request rebase remote repository stash versioning

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)
Gerrit Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Version Control Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
GitLab Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GitLab id=279 · gitlab

Aliases — catalog

  • GitLab (CANONICAL) primary

Context tags (catalog)

.gitlab-ci.yml CI/CD DevOps DevSecOps GitLab CI GitLab Pages GitLab Runner Kubernetes YAML artifact registry automated testing code review container registry issue boards issues merge requests monitoring pipelines repository management runners security scanning self-hosted version control webhooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Devops Platform
Vendor
GitLab Inc.
License
mit
Year introduced
2011
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: GitLab appears in many DevOps/CI-CD job descriptions and is widely used as an integrated source control and pipeline platform; its GitLab CI/CD and self-managed/SaaS offerings are common hiring signals.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
170
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)
LangChain Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LangChain id=240 · langchain

Aliases — catalog

  • LangChain (CANONICAL) primary

Context tags (catalog)

API integration Hugging Face LLM LLMs OpenAI RAG agents callbacks chains data augmentation deployment document loaders embeddings fine-tuning memory prompt engineering prompt templates prompts retrieval retrievers state management streaming text splitters toolkits tools vector database vector stores

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)
LlamaIndex Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LlamaIndex id=244 · llamaindex

Aliases — catalog

  • LlamaIndex (CANONICAL) primary
  • llama-index (VERSION)
  • llamaindex (VERSION)
  • llamaindex 0.10 (VERSION)
  • llamaindex 0.9 (VERSION)
  • llamaindex v0.10 (VERSION)
  • llamaindex v0.9 (VERSION)

Context tags (catalog)

API integration API support Hugging Face LLM integration LLM orchestration LangChain OpenAI RAG chunking custom data sources data connectors data indexing data pipelines document indexing document loaders document loading embedding embedding models embeddings fine-tuning indexing knowledge base knowledge graphs metadata management performance tuning prompt engineering prompt templates query engine query optimization querying real-time analytics real-time indexing retrieval-augmented generation retrievers scalability search optimization semantic search vector database vector databases vector store

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Llm Application Framework
Vendor
LlamaIndex
License
unknown
Year introduced
2023
Confidence
0.97
Version strategy
SEPARATE_ENTITY
Version tag
0.10

Maturity reasoning: LlamaIndex appears in growing numbers of LLM/RAG job postings and vendor docs, but it is still far less common than Python or LangChain, indicating rising adoption rather than universal demand.

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)
Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
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)
Deep Learning Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
CUDA Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Software Libraries
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
cuDNN Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Software Libraries
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
TensorRT Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: TensorRT id=261 · tensorrt

Aliases — catalog

  • TensorRT (CANONICAL) primary

Context tags (catalog)

C++ API CUDA FP16 INT8 NVIDIA GPU ONNX Python API batching deep learning inference engine serialization inference optimization latency model deployment quantization throughput

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Model Optimization Tool
Vendor
NVIDIA
License
proprietary
Year introduced
2016
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: TensorRT appears in a limited slice of ML inference JDs, mainly for NVIDIA GPU deployment; market demand is far below Python/PyTorch and concentrated in performance-critical roles.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
195
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Model Optimization and Acceleration Catalog dimension db id 53

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Model Optimization and Acceleration
model-optimization-and-acceleration
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
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)
Retrieval Augmented Generation new
RAG Architectures
rag-architectures
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Microservices in_db
Microservices and Distributed Systems
microservices-and-distributed-systems
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)
Helm in_db
Container Orchestration Platforms
container-orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Helm 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)
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)
Git in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitLab in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitLab in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
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)
LlamaIndex in_db
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TensorRT in_db
Model Optimization and Acceleration
model-optimization-and-acceleration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Large Language Models | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed LLM function calling | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=SHORT_LIVED
canonical_skill_proposed Information Retrieval | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Vector Databases | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Embedding models | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Rerank models | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=SHORT_LIVED
canonical_skill_proposed Gerrit | type=Version Control Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Deep Learning | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed CUDA | type=Software Libraries subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed cuDNN | type=Software Libraries subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed Retrieval Augmented Generation ↔ RAG Architectures
nano JD Parser — gpt-4.1-nano click to toggle
RoleSystem Software Engineer
CompanyNVIDIA
Experience5+ years of experience
DomainSoftware & SaaS Products
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "NVIDIA is committed to fostering",
      "last_5_words": "characteristic protected by law."
    },
    "text": "NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.",
    "word_count": 64
  },
  "certifications": [],
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    },
    "secondary": null
  },
  "education": [
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      "level": "Bachelor\u0027s",
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      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 5,
    "raw": "5+ years of experience"
  },
  "job_locations": [],
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  "role_aliases": [
    "Software Engineer",
    "Systems Engineer",
    "SWE"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 7,
      "heading": "What You\u2019ll Be Doing",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Architect, implement and optimize",
        "last_5_words": "in a collaborative team environment."
      },
      "text": "\u2022 Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow. Build a scalable microservice based architecture deployable on multi-node, multi-cloud environment\n\u2022 Designing, implementing and testing domain specific agents and workflows and a framework which can support multi-turn, multi-modal, multi-user conversations with a LLM driven agents.\n\u2022 Develop knowledge discovery, and reasoning capabilities including but not limited to disambiguation, clarification, and anticipation for dialogue systems\n\u2022 Analyze RAG and conversational AI agent end to end accuracy and limitations and recommend the next course of action \u0026 Improvements.\n\u2022 Characterize performance and quality metrics across platforms for various AI and system components\n\u2022 Collaborate with various teams on new product features and improvements of existing products. Customize and integrate the conversational AI framework with other NVIDIA products\n\u2022 Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.",
      "word_count": 198
    },
    {
      "bullet_count": 10,
      "heading": "What We Need To See",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Bachelor\u0027s degree or Master\u2019s",
        "last_5_words": "within a dynamic environment"
      },
      "text": "\u2022 Bachelor\u0027s degree or Master\u2019s degree (or equivalent experience) in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math\n\u2022 5+ years of experience\n\u2022 Excellent programming skills in Python\n\u2022 Hands-on experience of working with Retrieval Augmented Generation based applications\n\u2022 Knowhow of Large Language model applications, agentic workflows, LLM guardrails\n\u2022 Understanding of scalable deployment of LLM driven RAG and Agent applications in production environment\n\u2022 Familiarity with microservices, Docker, helm, kubernetes etc.\n\u2022 Experience of working on end to end Software lifecycle, release packaging \u0026 CI/CD pipeline\n\u2022 Hands-on experience on conversational AI Technologies like Large Language Model(LLM), LLM function calling, Information Retrieval, Vector Databases, Embedding and Rerank models, autonomous agents etc.\n\u2022 General background around version control and code review tools like Git, Gerrit, Gitlab.\n\u2022 Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic environment",
      "word_count": 174
    },
    {
      "bullet_count": 5,
      "heading": "Ways To Stand Out From The Crowd",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Strong fundamentals in Programming,",
        "last_5_words": "cloud, and embedded systems"
      },
      "text": "\u2022 Strong fundamentals in Programming, optimizations and Software design\n\u2022 Experience of working with open source frameworks like LangChain, LlamaIndex for building LLM driven applications\n\u2022 Strong knowledge of ML/DL techniques, algorithms and tools with exposure to Language Models\n\u2022 Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT\n\u2022 Background with deploying machine learning models on data center, cloud, and embedded systems",
      "word_count": 66
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "Retrieval Augmented Generation"
    },
    {
      "is_primary": true,
      "skill_name": "Microservices"
    },
    {
      "is_primary": true,
      "skill_name": "Docker"
    },
    {
      "is_primary": true,
      "skill_name": "Helm"
    },
    {
      "is_primary": true,
      "skill_name": "Kubernetes"
    },
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    },
    {
      "is_primary": true,
      "skill_name": "Large Language Models"
    },
    {
      "is_primary": true,
      "skill_name": "LLM function calling"
    },
    {
      "is_primary": true,
      "skill_name": "Information Retrieval"
    },
    {
      "is_primary": true,
      "skill_name": "Vector Databases"
    },
    {
      "is_primary": true,
      "skill_name": "Embedding models"
    },
    {
      "is_primary": true,
      "skill_name": "Rerank models"
    },
    {
      "is_primary": true,
      "skill_name": "Git"
    },
    {
      "is_primary": false,
      "skill_name": "Gerrit"
    },
    {
      "is_primary": false,
      "skill_name": "GitLab"
    },
    {
      "is_primary": true,
      "skill_name": "LangChain"
    },
    {
      "is_primary": true,
      "skill_name": "LlamaIndex"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Deep Learning"
    },
    {
      "is_primary": true,
      "skill_name": "CUDA"
    },
    {
      "is_primary": true,
      "skill_name": "cuDNN"
    },
    {
      "is_primary": true,
      "skill_name": "TensorRT"
    }
  ],
  "jd_role": {
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    "rationale": null,
    "role_aliases": [
      "Software Engineer",
      "Systems Engineer",
      "SWE"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "NVIDIA is committed to fostering",
        "last_5_words": "characteristic protected by law."
      },
      "text": "NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "NVIDIA",
    "ctc": null,
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        ],
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    },
    "education": [
      {
        "level": "Bachelor\u0027s",
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      }
    ],
    "experience": {
      "max": null,
      "min": 5,
      "raw": "5+ years of experience"
    },
    "job_locations": [],
    "role": "System Software Engineer",
    "role_aliases": [
      "Software Engineer",
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      "SWE"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 7,
        "heading": "What You\u2019ll Be Doing",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Architect, implement and optimize",
          "last_5_words": "in a collaborative team environment."
        },
        "text": "\u2022 Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow. Build a scalable microservice based architecture deployable on multi-node, multi-cloud environment\n\u2022 Designing, implementing and testing domain specific agents and workflows and a framework which can support multi-turn, multi-modal, multi-user conversations with a LLM driven agents.\n\u2022 Develop knowledge discovery, and reasoning capabilities including but not limited to disambiguation, clarification, and anticipation for dialogue systems\n\u2022 Analyze RAG and conversational AI agent end to end accuracy and limitations and recommend the next course of action \u0026 Improvements.\n\u2022 Characterize performance and quality metrics across platforms for various AI and system components\n\u2022 Collaborate with various teams on new product features and improvements of existing products. Customize and integrate the conversational AI framework with other NVIDIA products\n\u2022 Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.",
        "word_count": 198
      },
      {
        "bullet_count": 10,
        "heading": "What We Need To See",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Bachelor\u0027s degree or Master\u2019s",
          "last_5_words": "within a dynamic environment"
        },
        "text": "\u2022 Bachelor\u0027s degree or Master\u2019s degree (or equivalent experience) in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math\n\u2022 5+ years of experience\n\u2022 Excellent programming skills in Python\n\u2022 Hands-on experience of working with Retrieval Augmented Generation based applications\n\u2022 Knowhow of Large Language model applications, agentic workflows, LLM guardrails\n\u2022 Understanding of scalable deployment of LLM driven RAG and Agent applications in production environment\n\u2022 Familiarity with microservices, Docker, helm, kubernetes etc.\n\u2022 Experience of working on end to end Software lifecycle, release packaging \u0026 CI/CD pipeline\n\u2022 Hands-on experience on conversational AI Technologies like Large Language Model(LLM), LLM function calling, Information Retrieval, Vector Databases, Embedding and Rerank models, autonomous agents etc.\n\u2022 General background around version control and code review tools like Git, Gerrit, Gitlab.\n\u2022 Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic environment",
        "word_count": 174
      },
      {
        "bullet_count": 5,
        "heading": "Ways To Stand Out From The Crowd",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Strong fundamentals in Programming,",
          "last_5_words": "cloud, and embedded systems"
        },
        "text": "\u2022 Strong fundamentals in Programming, optimizations and Software design\n\u2022 Experience of working with open source frameworks like LangChain, LlamaIndex for building LLM driven applications\n\u2022 Strong knowledge of ML/DL techniques, algorithms and tools with exposure to Language Models\n\u2022 Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT\n\u2022 Background with deploying machine learning models on data center, cloud, and embedded systems",
        "word_count": 66
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "27dfab9e-23a4-4b41-86f7-9a3616035d52",
  "stage3_signals": {
    "alias_found": false,
    "alias_match_roles": [],
    "kra_match_roles": [
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Collaborate with various teams on new product features and improvements of existing products.",
            "similarity": 0.7017
          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.",
            "similarity": 0.5656
          },
          {
            "kra_text": "optimize responsiveness and performance",
            "sentence": "Characterize performance and quality metrics across platforms for various AI and system components",
            "similarity": 0.399
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 74,
        "score": 0.5554,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "kra_matches": [
          {
            "kra_text": "Defines evaluation frameworks, automated test suites, and human feedback loops to measure AI feature quality, accuracy, and consistency.",
            "sentence": "Characterize performance and quality metrics across platforms for various AI and system components",
            "similarity": 0.6001
          },
          {
            "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": "Customize and integrate the conversational AI framework with other NVIDIA products",
            "similarity": 0.5069
          },
          {
            "kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
            "sentence": "Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow.",
            "similarity": 0.5033
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 13,
        "score": 0.5368,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.",
            "similarity": 0.562
          },
          {
            "kra_text": "Designs multi-region and multi-availability-zone cloud infrastructure architectures for high availability, fault tolerance, and horizontal scalability.",
            "sentence": "Build a scalable microservice based architecture deployable on multi-node, multi-cloud environment",
            "similarity": 0.543
          },
          {
            "kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
            "sentence": "Background with deploying machine learning models on data center, cloud, and embedded systems",
            "similarity": 0.4812
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 0.5288,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Collaborate with various teams on new product features and improvements of existing products.",
            "similarity": 0.6095
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.",
            "similarity": 0.5474
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow.",
            "similarity": 0.419
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.5253,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Angular Frontend Developer",
        "kra_matches": [
          {
            "kra_text": "collaboration with design and QA",
            "sentence": "Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews and help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.",
            "similarity": 0.551
          },
          {
            "kra_text": "collaboration with design and QA",
            "sentence": "Collaborate with various teams on new product features and improvements of existing products.",
            "similarity": 0.5153
          },
          {
            "kra_text": "code review and refactoring",
            "sentence": "Strong fundamentals in Programming, optimizations and Software design",
            "similarity": 0.4688
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 90,
        "score": 0.5117,
        "slug": "angular-frontend-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 7,
        "matched_skills": [
          "CI/CD",
          "Kubernetes",
          "LangChain",
          "LlamaIndex",
          "Machine Learning",
          "Python",
          "TensorRT"
        ],
        "role_id": 3,
        "score": 0.3333,
        "slug": "ml-engineer",
        "total_count": 21
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "Kubernetes",
          "LangChain",
          "LlamaIndex",
          "Machine Learning",
          "Python"
        ],
        "role_id": 16,
        "score": 0.2381,
        "slug": "ml-ops-engineer",
        "total_count": 21
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "Docker",
          "Helm",
          "Python",
          "microservices"
        ],
        "role_id": 1,
        "score": 0.1905,
        "slug": "backend-engineer",
        "total_count": 21
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "CI/CD",
          "Docker",
          "Helm",
          "Kubernetes"
        ],
        "role_id": 10,
        "score": 0.1905,
        "slug": "devops-engineer",
        "total_count": 21
      },
      {
        "display_name": "AI Engineer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "LangChain",
          "LlamaIndex",
          "Machine Learning"
        ],
        "role_id": 13,
        "score": 0.1429,
        "slug": "ai-engineer",
        "total_count": 21
      }
    ]
  },
  "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": 0.95,
      "slug": "llm-genai-engineer",
      "total_count": null
    },
    "confidence": 0.95,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "RAG and agentic AI application engineering",
      "Scalable distributed GenAI systems",
      "Conversational AI workflow design",
      "LLM application deployment and operations",
      "Performance optimization and quality analysis",
      "Software lifecycle and CI/CD integration",
      "Cross-functional product integration"
    ],
    "matched_kras": [
      "Architect, implement and optimize GPU accelerated scalable RAG workflow",
      "Build a scalable microservice based architecture",
      "Designing, implementing and testing domain specific agents and workflows",
      "support multi-turn, multi-modal, multi-user conversations",
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      "Analyze RAG and conversational AI agent end to end accuracy",
      "Characterize performance and quality metrics across platforms",
      "Customize and integrate the conversational AI framework"
    ],
    "matched_skills": [
      "GPU accelerated",
      "Retrieval Augmented Generation(RAG)",
      "microservice based architecture",
      "multi-node, multi-cloud environment",
      "LLM driven agents",
      "large language model",
      "LLM function calling",
      "Information Retrieval",
      "Vector Databases",
      "Embedding and Rerank models",
      "LangChain",
      "LlamaIndex",
      "Docker",
      "helm",
      "kubernetes"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=AI / ML; The JD is centered on building and optimizing RAG, LLM-driven agents, multimodal conversational workflows, and GenAI application deployment, which aligns most strongly with an LLM/GenAI Engineer.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 7,
    "centroid_updated": true,
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    "new_kra_attached": {
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      "queue_id": 1327,
      "r_and_r_preview": "\u2022 Architect, implement and optimize GPU accelerated scalable Retrieval Augmented Generation(RAG) workflow. Build a scalable microservice based architecture deployable on multi-node, multi-cloud enviro",
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      "role_slug": "llm-genai-engineer",
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    },
    "new_skills_attached": [
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        "queue_id": 18119,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Retrieval Augmented Generation",
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        "is_primary": true,
        "queue_id": 18120,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Large Language Models",
        "status": "pending"
      },
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        "is_primary": true,
        "queue_id": 18122,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "LLM function calling",
        "status": "pending"
      },
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        "is_primary": true,
        "queue_id": 18124,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Information Retrieval",
        "status": "pending"
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        "is_primary": true,
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        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Vector Databases",
        "status": "pending"
      },
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        "is_primary": true,
        "queue_id": 18127,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Embedding models",
        "status": "pending"
      },
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        "is_primary": true,
        "queue_id": 18129,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Rerank models",
        "status": "pending"
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        "is_primary": false,
        "queue_id": 18131,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Gerrit",
        "status": "pending"
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      {
        "is_primary": true,
        "queue_id": 18133,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Deep Learning",
        "status": "pending"
      },
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        "is_primary": true,
        "queue_id": 18135,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "CUDA",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18136,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "cuDNN",
        "status": "pending"
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    ],
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    "v3_pipeline_triggered": false,
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    "v3_run_id": null
  }
}
API 2 — extract-details
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      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
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      "matched_canonical": {
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        "display_name": "Python",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": null,
      "existing_alias_text": null,
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      "matched_canonical": {
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        "skill_nature": "PATTERN",
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        "sub_category_id": 930,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
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      "matched_via": "embedding_display_name"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 178,
      "existing_alias_text": "microservices",
      "input_term": "Microservices",
      "matched_canonical": {
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        "display_name": "microservices",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "microservices",
        "sub_category_id": 1,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 198,
      "existing_alias_text": "Docker",
      "input_term": "Docker",
      "matched_canonical": {
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        "display_name": "Docker",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "docker",
        "sub_category_id": 63,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 204,
      "existing_alias_text": "Helm",
      "input_term": "Helm",
      "matched_canonical": {
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        "display_name": "Helm",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "helm",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1267,
      "existing_alias_text": "Kubernetes",
      "input_term": "Kubernetes",
      "matched_canonical": {
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        "display_name": "Kubernetes",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "kubernetes",
        "sub_category_id": 557,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1826,
      "existing_alias_text": "CI/CD",
      "input_term": "CI/CD",
      "matched_canonical": {
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        "display_name": "CI/CD",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "ci-cd",
        "sub_category_id": 900,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1613,
      "existing_alias_text": "Git",
      "input_term": "Git",
      "matched_canonical": {
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        "display_name": "Git",
        "id": 1002,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "git",
        "sub_category_id": 730,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 540,
      "existing_alias_text": "GitLab",
      "input_term": "GitLab",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "GitLab",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "gitlab",
        "sub_category_id": 170,
        "typical_lifespan": "EVERGREEN",
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      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 501,
      "existing_alias_text": "LangChain",
      "input_term": "LangChain",
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        "category_id": 5,
        "display_name": "LangChain",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "langchain",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 505,
      "existing_alias_text": "LlamaIndex",
      "input_term": "LlamaIndex",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "llamaindex",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2015,
      "existing_alias_text": "Machine Learning",
      "input_term": "Machine Learning",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 1024,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 522,
      "existing_alias_text": "TensorRT",
      "input_term": "TensorRT",
      "matched_canonical": {
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        "display_name": "TensorRT",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "tensorrt",
        "sub_category_id": 195,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
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      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
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      "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.",
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    },
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      "display_name": "Fullstack Developer",
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      "slug": "full-stack-engineer",
      "source": "db"
    },
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      "role_archetype": "Engineering",
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      "source": "db"
    },
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      "slug": "engineering-manager",
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    },
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      "display_name": "Cyber Security Engineer",
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      "slug": "cybersecurity-engineer",
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    },
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      "display_name": "Data Engineer",
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      "slug": "ml-ops-engineer",
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      "display_name": "AR/VR Engineer",
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      "slug": "ar-vr-engineer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
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      "role_archetype": "Engineering",
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      "display_name": "AI Engineer",
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      "slug": "ai-engineer",
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      "display_name": "Node.js Backend Developer",
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      "role_archetype": "Engineering",
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    {
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      "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": "Deep Learning",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "deep-learning",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "CUDA",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Software Libraries",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "cuda",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "cuDNN",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Software Libraries",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "cudnn",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "TensorRT",
          "alias_type": "CANONICAL",
          "id": 522,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "TensorRT",
        "id": 261,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "tensorrt",
        "sub_category_id": 195,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Model Optimization and Acceleration",
            "id": 53,
            "rationale": "Techniques for improving inference latency, throughput, memory use, and training efficiency. ML engineers use these methods to meet production constraints without sacrificing too much quality.",
            "slug": "model-optimization-and-acceleration",
            "source": "db"
          },
          "input_skill": "TensorRT",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "TensorRT",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Large Language Models",
    "LLM function calling",
    "Information Retrieval",
    "Vector Databases",
    "Embedding models",
    "Rerank models",
    "Gerrit",
    "Deep Learning",
    "CUDA",
    "cuDNN"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "LLM / GenAI Engineer",
    "id": 151,
    "rationale": "Domain=AI / ML; The JD is centered on building and optimizing RAG, LLM-driven agents, multimodal conversational workflows, and GenAI application deployment, which aligns most strongly with an LLM/GenAI Engineer.",
    "role_archetype": null,
    "slug": "llm-genai-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Retrieval Augmented Generation",
      "tag": "in_db"
    },
    {
      "skill": "Microservices",
      "tag": "in_db"
    },
    {
      "skill": "Docker",
      "tag": "in_db"
    },
    {
      "skill": "Helm",
      "tag": "in_db"
    },
    {
      "skill": "Kubernetes",
      "tag": "in_db"
    },
    {
      "skill": "CI/CD",
      "tag": "in_db"
    },
    {
      "skill": "Large Language Models",
      "tag": "new"
    },
    {
      "skill": "LLM function calling",
      "tag": "new"
    },
    {
      "skill": "Information Retrieval",
      "tag": "new"
    },
    {
      "skill": "Vector Databases",
      "tag": "new"
    },
    {
      "skill": "Embedding models",
      "tag": "new"
    },
    {
      "skill": "Rerank models",
      "tag": "new"
    },
    {
      "skill": "Git",
      "tag": "in_db"
    },
    {
      "skill": "Gerrit",
      "tag": "new"
    },
    {
      "skill": "GitLab",
      "tag": "in_db"
    },
    {
      "skill": "LangChain",
      "tag": "in_db"
    },
    {
      "skill": "LlamaIndex",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Deep Learning",
      "tag": "new"
    },
    {
      "skill": "CUDA",
      "tag": "new"
    },
    {
      "skill": "cuDNN",
      "tag": "new"
    },
    {
      "skill": "TensorRT",
      "tag": "in_db"
    }
  ],
  "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": "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": 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"
          }
        ],
        "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": "RAG Architectures",
          "id": 197,
          "rationale": "Retrieval-augmented generation patterns that ground model responses in external knowledge. This cluster covers how to fetch, chunk, rank, and inject context so AI features answer from product data instead of hallucinating.",
          "slug": "rag-architectures",
          "source": "db"
        },
        "dimension_id": 197,
        "input_skill": "Retrieval Augmented Generation",
        "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": "Microservices and Distributed Systems",
          "id": 9,
          "rationale": "Architectural patterns for decomposed backend systems and the operational concerns they introduce. Covers service boundaries, consistency tradeoffs, retries, circuit breakers, and distributed coordination.",
          "slug": "microservices-and-distributed-systems",
          "source": "db"
        },
        "dimension_id": 9,
        "input_skill": "Microservices",
        "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": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 41,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Containerization and Image Builds",
          "id": 152,
          "rationale": "Container image creation, tagging, hardening, and registry workflows used to package services for deployment. This is coherent because DevOps often owns the build-to-image path that feeds runtime environments.",
          "slug": "containerization-and-image-builds",
          "source": "db"
        },
        "dimension_id": 152,
        "input_skill": "Docker",
        "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": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 61,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Deployment and Cloud Platforms",
          "id": 418,
          "rationale": "Platform-as-a-Service and container environments for deploying Ruby applications.",
          "slug": "deployment-and-cloud-platforms",
          "source": "db"
        },
        "dimension_id": 418,
        "input_skill": "Docker",
        "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": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 61,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Deployment and Runtime Configuration",
          "id": 13,
          "rationale": "Configuration and release artifacts that control how backend services run in environments. Includes environment variables, manifests, feature flags, and release-safe configuration management.",
          "slug": "deployment-and-runtime-configuration",
          "source": "db"
        },
        "dimension_id": 13,
        "input_skill": "Docker",
        "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": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "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": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 61,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
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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.

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