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Pipeline run

0cdbcd3e-0a82-4fa2-a9ee-2fa9a936056e

Pipeline LLM cost (USD)
API 1: $0.0038 API 2: $0.0928 API 3: $0.0000 Total: $0.0966

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · API and service implementation
Build and ship end-to-end product features across backend and frontend, with a strong focus on LLM-powered APIs/RAG systems, scalable architecture, and production optimization for latency, reliability, and cost. Iterate quickly with founders/users, delivering clean, tested code from design to launch.
"Own end-to-end product development from zero-to-one, architecting major features across the full stack (design to production launch)."
Tech stack maturity
AI-Native & Bleeding-Edge
The skill set is centered on LLM application development, RAG, LLMOps, and vector databases, which strongly indicates an AI-native, cutting-edge backend engineering stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
· Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1): Claude, Gemini
Frameworks (×2): LangChain, LlamaIndex, OpenAI API, Weaviate, Pinecone
Models / concepts (×3): OpenAI, RAG, LLM, LLMOps, AI, ML, AI/ML
Evidence — skills matched in JD (17)
Node.js Python FastAPI TypeScript React Next.js LangChain LlamaIndex OpenAI API Claude API Gemini API RAG Pinecone Weaviate LLMOps Evaluation Frameworks Distributed Systems
Skill cluster (9 dimension groups, role-scoped)
Programming Languages
Python TypeScript
Web Application Frameworks
Node.js FastAPI
API Integration and Data Fetching
Claude API
LLM Serving & Deployment
LLMOps
Meta-Frameworks & SSR
Next.js
Performance and Scalability Tuning
Distributed Systems
React Component Architecture
React
Vector Databases
Pinecone
Cross-cutting / unaligned
LangChain LlamaIndex OpenAI API Gemini API RAG Weaviate Evaluation Frameworks
Show KRA description ↓
• Own end-to-end product development from zero-to-one, architecting major features across the full stack (design to production launch). • Collaborate with founders, users, and Customer Success to drive rapid iteration and product direction. • Design scalable, high-performance systems; implement robust components; optimize for reliability, latency, and cost at scale. • Deliver clean, production-grade, well-tested code in fast-paced environments. • 5–10 years in Backend, or Full Stack Engineering. • Strong CS fundamentals (CSE background preferred). • Deep experience with backend systems and APIs (e.g., Node.js, Python, FastAPI). • Proficiency in modern web stacks (e.g., TypeScript, React, Next.js). • Hands-on with LLM frameworks (LangChain, LlamaIndex), OpenAI/Claude/Gemini APIs, RAG pipelines, and vector databases (e.g., Pinecone, Weaviate). • Expertise in LLMOps, evaluation frameworks, and production optimization (latency, cost, distributed systems). • Proven track record building end-to-end systems from scratch in Series A/B+ startups or high-growth environments. • Experience with data-intensive apps, performance tuning, system design, and scalability. • Ability to rapidly adopt new tech and pivot across the stack.

Signals

Skill full-stack-engineer
0.35
Alias full-stack-engineer
0.57
KRA devops-engineer
0.39

Post-classification

Centroidupdated · n=32
Alias collision log#69
New-role queue
New skills captured4
New KRA capturedyes

Captured for admin review

Claude API primary Backend Engineer pending
Gemini API primary Backend Engineer pending
LLMOps primary Backend Engineer pending
Evaluation Frameworks primary Backend Engineer pending
R&R fragment (sim 0.00) Backend Engineer pending

• Own end-to-end product development from zero-to-one, architecting major features across the full stack (design to production launch). • Collaborate with founders, users, and Customer Success to driv…

Status: completed Created: 2026-05-20T05:17:09.943853Z Updated: 2026-05-20T05:19:16.871253Z API 3 duration: 4008 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

Full Stack Engineer

CASE D

slug: full-stack-engineer · id: 15 · source: db

The role of Full Stack Engineer covers all key primary skills from Node.js to React and TypeScript.

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

4
New skills
7
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

Role: Staff Engineer / SDE-3 (Backend/Full Stack)

Location: Bengaluru

Work Type: Full-Time

Experience: 6–10 Years

Domain: AI/ML / Backend / Full Stack Engineering

Pinegap

Pinegap is building AI agents for Wall Street funds focused on fundamental equity investing, powering long/short hedge funds, long-only mutual funds, RIAs, and wealth managers.

Co-founder Ankit brings 15 years of Wall Street experience (7 years at J.P. Morgan, 8 years at a long/short hedge fund). Pinegap raised its seed in 2024, onboarded hundreds of clients, and recently closed a Series A—now accelerating growth with immediate senior engineering hires.

What You'll Do
• Own end-to-end product development from zero-to-one, architecting major features across the full stack (design to production launch).
• Collaborate with founders, users, and Customer Success to drive rapid iteration and product direction.
• Design scalable, high-performance systems; implement robust components; optimize for reliability, latency, and cost at scale.
• Deliver clean, production-grade, well-tested code in fast-paced environments.

Technical Requirements
• 5–10 years in Backend, or Full Stack Engineering.
• Strong CS fundamentals (CSE background preferred).
• Deep experience with backend systems and APIs (e.g., Node.js, Python, FastAPI).
• Proficiency in modern web stacks (e.g., TypeScript, React, Next.js).
• Hands-on with LLM frameworks (LangChain, LlamaIndex), OpenAI/Claude/Gemini APIs, RAG pipelines, and vector databases (e.g., Pinecone, Weaviate).
• Expertise in LLMOps, evaluation frameworks, and production optimization (latency, cost, distributed systems).

Other Requirements
• Proven track record building end-to-end systems from scratch in Series A/B+ startups or high-growth environments.
• Experience with data-intensive apps, performance tuning, system design, and scalability.
• Ability to rapidly adopt new tech and pivot across the stack.

Backend Engineering,AI/ML & LLM Expertise,Full Stack Development

Skills from this JD

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

Node.js Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Node.js id=1564 · node-js

Aliases — catalog

  • Node.js (CANONICAL) primary
  • Node 20 LTS (VERSION)
  • Node 20.x (VERSION)
  • Node.js 20 (VERSION)
  • Node.js 20 LTS (VERSION)
  • Node.js 20.x (VERSION)
  • node (VERSION)
  • node 10 (VERSION)
  • node 12 (VERSION)
  • node 14 (VERSION)
  • node 16 (VERSION)
  • node 18 (VERSION)
  • node 20 (VERSION)
  • node v10 (VERSION)
  • node v12 (VERSION)
  • node v14 (VERSION)
  • node v16 (VERSION)
  • node v18 (VERSION)
  • node v20 (VERSION)
  • node.js (VERSION)
  • node.js 10 (VERSION)
  • node.js 12 (VERSION)
  • node.js 14 (VERSION)
  • node.js 16 (VERSION)
  • node.js 18 (VERSION)
  • node.js 20 (VERSION)
  • nodejs (VERSION)
  • nodejs 10 (VERSION)
  • nodejs 12 (VERSION)
  • nodejs 14 (VERSION)
  • nodejs 16 (VERSION)
  • nodejs 18 (VERSION)
  • nodejs 20 (VERSION)

Context tags (catalog)

API Asynchronous Event-driven Express GraphQL JavaScript Jest Koa Microservices Middleware Mocha MongoDB Mongoose NestJS RESTful Socket.io TypeScript npm

Stored enrichment (catalog DB)

Category
Runtime
Sub-category
Javascript Runtime
Vendor
OpenJS Foundation
License
mit
Year introduced
2009
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
20

Maturity reasoning: Node.js appears in high-volume job postings across backend/full-stack roles and is a standard runtime in major cloud/vendor docs and ecosystem tooling, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
RUNTIME
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
67
Sub-category id
1036
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Full Stack Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension saved
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)

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Full Stack Engineer

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension saved
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)
FastAPI Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: FastAPI id=1201 · fastapi

Aliases — catalog

  • FastAPI (CANONICAL) primary

Context tags (catalog)

API documentation ASGI CORS JSON JSON Schema OAuth2 OpenAPI Pydantic RESTful Starlette UVicorn WebSocket async async programming data validation dependency injection middleware path parameters query parameters type hints uvicorn

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Sebastián Ramírez
License
mit
Year introduced
2018
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: FastAPI appears in many Python backend job postings and has strong GitHub adoption; it’s now a common choice for API development alongside Flask/Django rather than a niche tool.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

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)
TypeScript Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: TypeScript id=524 · typescript

Aliases — catalog

  • TypeScript (CANONICAL) primary
  • TypeScript 5 (VERSION)
  • TypeScript 5.x (VERSION)
  • ts (VERSION)
  • ts5 (VERSION)
  • typescript 5 (VERSION)
  • typescript 5.x (VERSION)
  • typescript5 (VERSION)

Context tags (catalog)

Angular Async/Await Decorator Deno ESLint Generics GraphQL Interfaces JavaScript Jest NestJS Next.js Node.js Promise React Redux RxJS TypeORM Vite Vue Vue.js Webpack async/await decorators generics interfaces module resolution npm strict mode tsconfig type annotations type guards type inference type safety yarn

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Microsoft
License
apache_2
Year introduced
2012
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: TypeScript is a hiring-pipeline staple: it appears in a large share of modern web/frontend and Node.js job descriptions, and major frameworks like Angular and Next.js recommend it by default.

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)

  • Cross-Platform App Languages Catalog dimension db id 167

    Library dimension (catalog)

    Roles linked in library: Hybrid Mobile Developer

  • JavaScript and TypeScript Catalog dimension db id 114

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Full Stack Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cross-Platform App Languages
cross-platform-app-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JavaScript and TypeScript
javascript-and-typescript
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension saved
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: React id=610 · react

Aliases — catalog

  • React (CANONICAL) primary
  • React 0.13 (VERSION)
  • React 0.14 (VERSION)
  • React 15 (VERSION)
  • React 15.x (VERSION)
  • React 16 (VERSION)
  • React 16.x (VERSION)
  • React 17 (VERSION)
  • React 17.x (VERSION)
  • React 18 (VERSION)
  • React 18.x (VERSION)
  • React 19 (VERSION)
  • React v15 (VERSION)
  • React v16 (VERSION)
  • React v17 (VERSION)
  • React v18 (VERSION)
  • React v19 (VERSION)
  • ReactJS 18 (VERSION)
  • react 15 (VERSION)
  • react 16 (VERSION)
  • react 17 (VERSION)
  • react 18 (VERSION)
  • react 19 (VERSION)
  • react15 (VERSION)
  • react16 (VERSION)
  • react17 (VERSION)
  • react18 (VERSION)
  • react19 (VERSION)
  • reactjs 18 (VERSION)

Context tags (catalog)

Babel Class Components Component Lifecycle Context API Functional Components Higher-Order Components Hooks JSX Next.js PropTypes Props React Native React Router Redux SSR State Management Styled Components Testing Library TypeScript Virtual DOM Webpack component lifecycle context API frontend hooks props state management useEffect useState virtual DOM

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Frontend Framework
Vendor
Meta
License
mit
Year introduced
2013
Confidence
0.98
Version strategy
SEPARATE_ENTITY
Version tag
18

Maturity reasoning: React appears in high-volume frontend job postings across startups and enterprises and remains a default hiring-pipeline skill, with strong GitHub/npm usage and ecosystem activity.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1072
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • UI Frameworks and Rendering Catalog dimension db id 115

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer, Full Stack Engineer, Hybrid Mobile Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
UI Frameworks and Rendering
ui-frameworks-and-rendering
Existing dimension (library) · Role↔dimension saved
Next.js Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Next.js id=705 · next-js

Aliases — catalog

  • Next.js (CANONICAL) primary
  • Next 10 (VERSION)
  • Next 11 (VERSION)
  • Next 12 (VERSION)
  • Next 13 (VERSION)
  • Next 14 (VERSION)
  • Next 15 (VERSION)
  • Next 2 (VERSION)
  • Next 3 (VERSION)
  • Next 4 (VERSION)
  • Next 5 (VERSION)
  • Next 6 (VERSION)
  • Next 7 (VERSION)
  • Next 8 (VERSION)
  • Next 9 (VERSION)
  • Next.js 1 (VERSION)
  • Next.js 10 (VERSION)
  • Next.js 11 (VERSION)
  • Next.js 12 (VERSION)
  • Next.js 13 (VERSION)
  • Next.js 14 (VERSION)
  • Next.js 15 (VERSION)
  • Next.js 2 (VERSION)
  • Next.js 3 (VERSION)
  • Next.js 4 (VERSION)
  • Next.js 5 (VERSION)
  • Next.js 6 (VERSION)
  • Next.js 7 (VERSION)
  • Next.js 8 (VERSION)
  • Next.js 9 (VERSION)
  • next (VERSION)
  • next.js (VERSION)
  • next.js 14 (VERSION)
  • nextjs (VERSION)
  • nextjs 14 (VERSION)

Context tags (catalog)

API routes App Router CSS-in-JS Client Components ISR JAMstack Pages Router React SSG SSR Server Components Tailwind CSS TypeScript Vercel Webpack dynamic routing getServerSideProps getStaticProps headless CMS incremental static regeneration middleware server-side rendering static generation webpack

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Vercel
License
mit
Year introduced
2016
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Next.js appears in many frontend/full-stack job descriptions and is a common React meta-framework for production apps; Vercel’s ecosystem and strong GitHub adoption signal broad market demand.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Meta-Frameworks & SSR Catalog dimension db id 130

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer

  • UI Frameworks and Rendering Catalog dimension db id 115

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer, Full Stack Engineer, Hybrid Mobile Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Meta-Frameworks & SSR
meta-frameworks-ssr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
UI Frameworks and Rendering
ui-frameworks-and-rendering
Existing dimension (library) · Role↔dimension saved
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, ML Ops 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, ML Ops 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)
OpenAI API Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: OpenAI API id=1223 · openai-api

Aliases — catalog

  • OpenAI API (CANONICAL) primary

Context tags (catalog)

API documentation API key ChatGPT GPT-3 OpenAI Playground completion embeddings fine-tuning integration model training natural language processing prompt engineering rate limits text generation webhooks

Stored enrichment (catalog DB)

Category
Service
Sub-category
Llm Api Service
Vendor
OpenAI
License
other_open
Year introduced
2020
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Strong JD signal: OpenAI API is now commonly listed in AI/ML and full-stack roles, and OpenAI’s own platform docs and ecosystem integrations show broad production adoption.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
1006
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • LLM Provider APIs Catalog dimension db id 195

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
LLM Provider APIs
llm-provider-apis
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Claude API Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.86

Claude API is increasingly appearing in AI/LLM job postings and vendor docs, but it is not yet as universal as AWS/OpenAI APIs; market signal shows rapid adoption rather than broad staple status.

Vendor & license

Anthropic ·unknown ·since 2023 (0.85)

Context keywords
API integration natural language processing machine learning chatbot development text generation data analysis RESTful services authentication JSON webhooks real-time processing model training deployment scalability user input handling
Ambiguity low

“Claude API” is a specific vendor/product name (Anthropic) and is unlikely to be confused with other catalog services.

Versioning

Not versioned

Type assignment

Service ·ai_model_api_service confidence 0.93

By the Service vs Platform rule, Claude API is a specific managed capability consumed inside Anthropic's hosted platform rather than software you run yourself.

Derived legacy fields
Category
Service
Sub-category
ai_model_api_service
Skill nature
CLOUD_SERVICE
Volatility
EMERGING
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • API Integration and Data Fetching Catalog dimension db id 127

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer, Full Stack Engineer

  • API Integration and Data Fetching Catalog dimension db id 127

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer, Full Stack Engineer

Locked dimensions (v3 placement)

  • LLM API Integration

    Pipeline tentative id

    Client and server integration with hosted large language model APIs for prompting, response handling, and application workflows. Claude API belongs here because it is a vendor LLM interface used to send prompts, receive completions, and manage model-specific request options.

  • API Integration and Data Fetching

    Reuses catalog slug

    Client-side integration with backend endpoints and third-party services, including request shaping, response handling, and synchronization with application state. Claude API fits when the skill is about consuming an external HTTP API rather than LLM-specific prompting patterns.

  • API Integration and Data Fetching

    Reuses catalog slug

    Client-side integration with backend endpoints and third-party services, including request shaping, response handling, and synchronization with UI state. This is central to frontend work because most screens depend on remote data.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
API Integration and Data Fetching
api-integration-and-data-fetching
New skill saved · Existing dimension (library) · Role↔dimension saved
Gemini API Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.84

Gemini API is increasingly appearing in AI/LLM job postings and Google’s developer docs/SDKs show active investment, but it’s not yet as universal as OpenAI or AWS APIs.

Vendor & license

Gemini ·unknown ·since 2023 (0.80)

Context keywords
OAuth RESTful JSON endpoint authentication rate limiting webhooks SDK API key versioning payload response format error handling throttling documentation
Ambiguity low

“Gemini API” is a specific Google/Google AI model API name; unlikely to be confused with other distinct model APIs in typical JDs.

Versioning

Not versioned

Type assignment

Service ·ai_model_api confidence 0.93

By the Service vs Platform rule, Gemini API is a specific managed capability offered within a larger hosted environment rather than software-you-run, so it fits Service.

Derived legacy fields
Category
Service
Sub-category
ai_model_api
Skill nature
CLOUD_SERVICE
Volatility
EMERGING
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Generative AI API Integration

    Pipeline tentative id

    Integration of hosted generative AI APIs into applications, including request/response shaping, prompt construction, streaming, and error handling. Gemini API belongs here because it is a vendor LLM interface used to call model capabilities from backend or full-stack systems.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RAG Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RAG id=1194 · rag

Aliases — catalog

  • RAG (CANONICAL)

Context tags (catalog)

AI applications contextualization data augmentation fine-tuning generation information retrieval knowledge integration machine learning model training natural language processing prompt engineering retrieval semantic search transformer models user intent

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Retrieval Augmented Generation
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: RAG appears in many recent AI/ML job descriptions and vendor docs, but it is still not a universal baseline skill like Python or SQL; market demand is rising fast rather than fully standardized.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
904
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pinecone Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Pinecone id=242 · pinecone

Aliases — catalog

  • Pinecone (CANONICAL) primary

Context tags (catalog)

ANN API integration LangChain OpenAI embeddings RAG analytics cloud-native data pipelines data retrieval distributed architecture embedding embeddings high-dimensional data indexing machine learning metadata filtering metadata management namespace nearest neighbor performance tuning query optimization real-time indexing retrieval augmented generation scalability semantic search similarity search upsert vector index vector search

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Vector Database Platform
Vendor
Pinecone
License
unknown
Year introduced
2021
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Pinecone appears in a growing number of AI/vector-search job postings and vendor docs, but it is still far from universal compared with PostgreSQL or AWS.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
177
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • LLM Operations and Orchestration Catalog dimension db id 49

    Library dimension (catalog)

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

  • Vector Databases Catalog dimension db id 198

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vector Databases
vector-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Weaviate Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Weaviate id=1242 · weaviate

Aliases — catalog

  • Weaviate (CANONICAL) primary

Context tags (catalog)

KNN RESTful API cloud-native data ingestion data modeling embedding graphQL machine learning open-source real-time analytics scalability schema semantic search vector search vectorization

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Vector Database Platform
Vendor
Weaviate
License
apache_2
Year introduced
2018
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Weaviate appears in a growing number of AI/vector-search job postings and vendor docs, but JD volume is still far below PostgreSQL/AWS-level staples; GitHub activity and ecosystem integrations are rising.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
177
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Vector Databases Catalog dimension db id 198

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Vector Databases
vector-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LLMOps Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.84

LLMOps appears increasingly in job descriptions and vendor docs for deploying and monitoring LLM apps, but it is not yet a universal hiring staple like AWS/Kubernetes.

Vendor & license

(0.95)

Context keywords
model deployment MLOps scalability monitoring versioning A/B testing data pipelines inference API integration cloud infrastructure CI/CD model management performance tuning containerization orchestration
Ambiguity low

LLMOps is a specific term for LLM deployment/operations; it’s unlikely to be confused with other catalog skills.

Versioning

Not versioned

Type assignment

Methodology ·llm_operations_methodology confidence 0.93

LLMOps is fundamentally a way of working for operating, deploying, and monitoring LLM systems, so by the Concept vs Methodology rule it fits Methodology rather than a tool or platform.

Derived legacy fields
Category
Methodology
Sub-category
llm_operations_methodology
Skill nature
METHODOLOGY
Volatility
EMERGING
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • LLM Serving & Deployment Catalog dimension db id 209

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Guardrails and Safety Controls Catalog dimension db id 203

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Experiment Tracking and Evaluation Catalog dimension db id 44

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

  • Experiment Tracking and Evaluation Catalog dimension db id 44

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

  • LLM Serving & Deployment Catalog dimension db id 209

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Guardrails and Safety Controls Catalog dimension db id 203

    Library dimension (catalog)

    Roles linked in library: AI Engineer

Locked dimensions (v3 placement)

  • LLM Serving and Deployment

    Reuses catalog slug

    Production hosting, rollout, and runtime operation of large language models. LLMOps belongs here because it includes the deployment mechanics, scaling, and serving reliability needed to expose LLMs as usable services.

  • Guardrails and Safety Controls

    Reuses catalog slug

    Runtime controls that constrain LLM behavior, reduce harmful outputs, and enforce product policy. LLMOps often includes these controls because operating LLMs in production requires safety filters, policy checks, and abuse mitigation.

  • Experiment Tracking and Evaluation

    Reuses catalog slug

    Measurement, comparison, and validation of model and prompt changes before and after release. LLMOps belongs here when it refers to evaluating prompts, models, and system behavior with repeatable metrics and test sets.

  • Experiment Tracking and Evaluation

    Reuses catalog slug

    Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.

  • LLM Serving & Deployment

    Reuses catalog slug

    Tools and frameworks for hosting and serving LLM models in production environments.

  • Guardrails and Safety Controls

    Reuses catalog slug

    Runtime controls that constrain model behavior, block unsafe outputs, and enforce product policy. This is a core AI Engineer responsibility because the role owns fallback behavior, refusal logic, and safe response shaping in production.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
LLM Serving & Deployment
llm-serving-deployment
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Guardrails and Safety Controls
guardrails-and-safety-controls
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Experiment Tracking and Evaluation
experiment-tracking-and-evaluation
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Evaluation Frameworks Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity emerging confidence 0.78

Appears increasingly in ML/LLM job descriptions and vendor docs, but there’s no single dominant standard yet; adoption is growing around tools like LangSmith, Ragas, and OpenAI Evals.

Vendor & license

(0.50)

Context keywords
metrics A/B testing performance indicators data analysis feedback loops user research statistical significance experimental design control groups hypothesis testing analytics tools iteration qualitative assessment quantitative analysis reporting frameworks
Ambiguity low

“Evaluation Frameworks” is a specific, standard ML/experimentation concept; unlikely to be confused with other distinct catalog skills.

Versioning

Not versioned

Type assignment

Framework ·evaluation_framework confidence 0.78

By the Tool vs Framework rule, this refers to a structured codebase or set of abstractions used to build and run evaluations inside, rather than a standalone user-operated tool.

Derived legacy fields
Category
Framework
Sub-category
evaluation_framework
Skill nature
FRAMEWORK
Volatility
EMERGING
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Experiment Tracking and Evaluation Catalog dimension db id 44

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

  • Experiment Tracking and Evaluation Catalog dimension db id 44

    Library dimension (catalog)

    Roles linked in library: ML Engineer, ML Ops Engineer

Locked dimensions (v3 placement)

  • Experiment Tracking and Evaluation

    Reuses catalog slug

    Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This skill belongs here because evaluation frameworks define how results are measured, benchmarked, and judged across experiments.

  • Experiment Tracking and Evaluation

    Reuses catalog slug

    Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Experiment Tracking and Evaluation
experiment-tracking-and-evaluation
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Distributed Systems Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Distributed Systems id=1369 · distributed-systems

Aliases — catalog

  • Distributed Systems (CANONICAL)

Context tags (catalog)

CAP theorem Docker Swarm Kafka MapReduce Zookeeper consensus algorithms distributed databases eventual consistency fault tolerance gRPC load balancing message queues microservices replication sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Distributed Systems
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common hiring requirement in backend/platform JDs at large tech firms; appears across AWS, Kafka, microservices, and systems roles, with strong GitHub/Stack Overflow activity and no sunset signal.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1035
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, Full Stack Engineer, ML Engineer, ML Ops Engineer

  • Performance and Scalability Tuning Catalog dimension db id 11

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Performance and Scalability Tuning
performance-and-scalability-tuning
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)

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
Node.js in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension saved
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension saved
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)
FastAPI in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TypeScript in_db
Cross-Platform App Languages
cross-platform-app-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TypeScript in_db
JavaScript and TypeScript
javascript-and-typescript
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TypeScript in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension saved
TypeScript in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React in_db
UI Frameworks and Rendering
ui-frameworks-and-rendering
Existing dimension (library) · Role↔dimension saved
Next.js in_db
Meta-Frameworks & SSR
meta-frameworks-ssr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Next.js in_db
UI Frameworks and Rendering
ui-frameworks-and-rendering
Existing dimension (library) · Role↔dimension saved
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)
OpenAI API in_db
LLM Provider APIs
llm-provider-apis
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RAG in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pinecone in_db
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pinecone in_db
Vector Databases
vector-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Weaviate in_db
Vector Databases
vector-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Distributed Systems in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Distributed Systems in_db
Performance and Scalability Tuning
performance-and-scalability-tuning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Distributed Systems in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Claude API in_db
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Claude API in_db
API Integration and Data Fetching
api-integration-and-data-fetching
New skill saved · Existing dimension (library) · Role↔dimension saved
Gemini API in_db
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LLMOps in_db
LLM Serving & Deployment
llm-serving-deployment
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LLMOps in_db
Guardrails and Safety Controls
guardrails-and-safety-controls
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LLMOps in_db
Experiment Tracking and Evaluation
experiment-tracking-and-evaluation
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Evaluation Frameworks in_db
Experiment Tracking and Evaluation
experiment-tracking-and-evaluation
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_added Claude API 1632
canonical_skill_added Gemini API 1633
canonical_skill_added LLMOps 1634
canonical_skill_added Evaluation Frameworks 1635
dimension_skill_link Claude API ↔ React Frontend Development 96
dimension_skill_link Claude API ↔ API Integration and Data Fetching 127
dimension_skill_link Gemini API ↔ React Frontend Development 96
dimension_skill_link LLMOps ↔ LLM Serving & Deployment 209
dimension_skill_link LLMOps ↔ Guardrails and Safety Controls 203
dimension_skill_link LLMOps ↔ Experiment Tracking and Evaluation 44
dimension_skill_link Evaluation Frameworks ↔ Experiment Tracking and Evaluation 44
nano JD Parser — gpt-4.1-nano click to toggle
RoleStaff Engineer / SDE-3 (Backend/Full Stack)
CompanyPinegap
Experience6–10 Years
DomainSoftware & SaaS Products
Location Bengaluru, India (onsite)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Pinegap is building AI agents",
      "last_5_words": "immediate senior engineering hires."
    },
    "text": "Pinegap is building AI agents for Wall Street funds focused on fundamental equity investing, powering long/short hedge funds, long-only mutual funds, RIAs, and wealth managers.\nCo-founder Ankit brings 15 years of Wall Street experience (7 years at J.P. Morgan, 8 years at a long/short hedge fund). Pinegap raised its seed in 2024, onboarded hundreds of clients, and recently closed a Series A\u2014now accelerating growth with immediate senior engineering hires.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "Pinegap",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "SaaS",
        "Product Companies"
      ],
      "domain": "Software \u0026 SaaS Products"
    },
    "secondary": {
      "aliases": [
        "Artificial Intelligence",
        "Machine Learning"
      ],
      "domain": "AI/ML"
    }
  },
  "education": [],
  "experience": {
    "max": 10,
    "min": 6,
    "raw": "6\u201310 Years"
  },
  "job_locations": [
    {
      "aliases": [
        "Bangalore"
      ],
      "city": "Bengaluru",
      "country": "India",
      "state": null,
      "work_mode": "onsite"
    }
  ],
  "role": "Staff Engineer / SDE-3 (Backend/Full Stack)",
  "role_aliases": [
    "Staff Engineer",
    "SDE-3",
    "Senior Software Engineer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 4,
      "heading": "What You\u0027ll Do",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Own end-to-end product development",
        "last_5_words": "code in fast-paced environments."
      },
      "text": "\u2022 Own end-to-end product development from zero-to-one, architecting major features across the full stack (design to production launch).\n\u2022 Collaborate with founders, users, and Customer Success to drive rapid iteration and product direction.\n\u2022 Design scalable, high-performance systems; implement robust components; optimize for reliability, latency, and cost at scale.\n\u2022 Deliver clean, production-grade, well-tested code in fast-paced environments.",
      "word_count": 56
    },
    {
      "bullet_count": 6,
      "heading": "Technical Requirements",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 5\u201310 years in Backend, or",
        "last_5_words": "cost, distributed systems)."
      },
      "text": "\u2022 5\u201310 years in Backend, or Full Stack Engineering.\n\u2022 Strong CS fundamentals (CSE background preferred).\n\u2022 Deep experience with backend systems and APIs (e.g., Node.js, Python, FastAPI).\n\u2022 Proficiency in modern web stacks (e.g., TypeScript, React, Next.js).\n\u2022 Hands-on with LLM frameworks (LangChain, LlamaIndex), OpenAI/Claude/Gemini APIs, RAG pipelines, and vector databases (e.g., Pinecone, Weaviate).\n\u2022 Expertise in LLMOps, evaluation frameworks, and production optimization (latency, cost, distributed systems).",
      "word_count": 83
    },
    {
      "bullet_count": 3,
      "heading": "Other Requirements",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Proven track record building end-to-end",
        "last_5_words": "and pivot across the stack."
      },
      "text": "\u2022 Proven track record building end-to-end systems from scratch in Series A/B+ startups or high-growth environments.\n\u2022 Experience with data-intensive apps, performance tuning, system design, and scalability.\n\u2022 Ability to rapidly adopt new tech and pivot across the stack.",
      "word_count": 42
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Node.js"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "FastAPI"
    },
    {
      "is_primary": true,
      "skill_name": "TypeScript"
    },
    {
      "is_primary": true,
      "skill_name": "React"
    },
    {
      "is_primary": true,
      "skill_name": "Next.js"
    },
    {
      "is_primary": true,
      "skill_name": "LangChain"
    },
    {
      "is_primary": true,
      "skill_name": "LlamaIndex"
    },
    {
      "is_primary": true,
      "skill_name": "OpenAI API"
    },
    {
      "is_primary": true,
      "skill_name": "Claude API"
    },
    {
      "is_primary": true,
      "skill_name": "Gemini API"
    },
    {
      "is_primary": true,
      "skill_name": "RAG"
    },
    {
      "is_primary": true,
      "skill_name": "Pinecone"
    },
    {
      "is_primary": true,
      "skill_name": "Weaviate"
    },
    {
      "is_primary": true,
      "skill_name": "LLMOps"
    },
    {
      "is_primary": true,
      "skill_name": "Evaluation Frameworks"
    },
    {
      "is_primary": true,
      "skill_name": "Distributed Systems"
    }
  ],
  "jd_role": {
    "display_name": "Staff Engineer / SDE-3 (Backend/Full Stack)",
    "rationale": null,
    "role_aliases": [
      "Staff Engineer",
      "SDE-3",
      "Senior Software Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Pinegap is building AI agents",
        "last_5_words": "immediate senior engineering hires."
      },
      "text": "Pinegap is building AI agents for Wall Street funds focused on fundamental equity investing, powering long/short hedge funds, long-only mutual funds, RIAs, and wealth managers.\nCo-founder Ankit brings 15 years of Wall Street experience (7 years at J.P. Morgan, 8 years at a long/short hedge fund). Pinegap raised its seed in 2024, onboarded hundreds of clients, and recently closed a Series A\u2014now accelerating growth with immediate senior engineering hires.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "Pinegap",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "SaaS",
          "Product Companies"
        ],
        "domain": "Software \u0026 SaaS Products"
      },
      "secondary": {
        "aliases": [
          "Artificial Intelligence",
          "Machine Learning"
        ],
        "domain": "AI/ML"
      }
    },
    "education": [],
    "experience": {
      "max": 10,
      "min": 6,
      "raw": "6\u201310 Years"
    },
    "job_locations": [
      {
        "aliases": [
          "Bangalore"
        ],
        "city": "Bengaluru",
        "country": "India",
        "state": null,
        "work_mode": "onsite"
      }
    ],
    "role": "Staff Engineer / SDE-3 (Backend/Full Stack)",
    "role_aliases": [
      "Staff Engineer",
      "SDE-3",
      "Senior Software Engineer"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 4,
        "heading": "What You\u0027ll Do",
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        "source_marker": {
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}
API 2 — extract-details
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        "display_name": "LLM Provider APIs",
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        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
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        {
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        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
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    {
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        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
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        {
          "display_name": "Full Stack Engineer",
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          "rationale": null,
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        "slug": "d_init_01",
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      "input_skill": "Gemini API",
      "llm_role": null,
      "roles_from_db": []
    },
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        "difficulty_hint": "well_known",
        "display_name": "LLM Serving \u0026 Deployment",
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        "rationale": "Tools and frameworks for hosting and serving LLM models in production environments.",
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      "input_skill": "LLMOps",
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        "rationale": "Runtime controls that constrain model behavior, block unsafe outputs, and enforce product policy. This is a core AI Engineer responsibility because the role owns fallback behavior, refusal logic, and safe response shaping in production.",
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      "input_skill": "LLMOps",
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        "rationale": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.",
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      "input_skill": "LLMOps",
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        {
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          "rationale": null,
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          "slug": "ml-ops-engineer",
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        "rationale": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.",
        "slug": "experiment-tracking-and-evaluation",
        "source": "db"
      },
      "input_skill": "LLMOps",
      "llm_role": null,
      "roles_from_db": [
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
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        },
        {
          "display_name": "ML Ops Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-ops-engineer",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "LLM Serving \u0026 Deployment",
        "id": 209,
        "rationale": "Tools and frameworks for hosting and serving LLM models in production environments.",
        "slug": "llm-serving-deployment",
        "source": "db"
      },
      "input_skill": "LLMOps",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AI Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ai-engineer",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Guardrails and Safety Controls",
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        "rationale": "Runtime controls that constrain model behavior, block unsafe outputs, and enforce product policy. This is a core AI Engineer responsibility because the role owns fallback behavior, refusal logic, and safe response shaping in production.",
        "slug": "guardrails-and-safety-controls",
        "source": "db"
      },
      "input_skill": "LLMOps",
      "llm_role": null,
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            "reasoning": "Dim A covers production LLM serving/deployment: LLMOps, model deployment, inference scaling, canary releases, GPU inference serving, request routing, and serving observability. Dim B covers experiment tracking and evaluation: recording runs, comparing metrics, reproducibility, artifacts, and offline evaluation before release. career-track: no, because senior LLM serving/deployment work is production infrastructure/ops, while senior experiment-tracking/evaluation work is experimentation and assessment tooling; the exemplar skills do not overlap.",
            "similarity": 0.6432787852207635
          },
          {
            "a_dim_id": "guardrails-and-safety-controls",
            "a_name": "Guardrails and Safety Controls",
            "a_role": "__skill_focal__",
            "b_dim_id": "experiment-tracking-and-evaluation",
            "b_name": "Experiment Tracking and Evaluation",
            "b_role": "__skill_focal__",
            "pair_kind": "intra_role",
            "reasoning": "Dim A is runtime safety enforcement: policy checks, prompt injection defense, output moderation, safety classifiers, jailbreak mitigation, and abuse prevention. Dim B is experiment tracking/evaluation: recording runs, comparing experiments, reproducibility, metrics, artifacts, and offline evaluation. These are different lifecycle stages and skill sets. career-track: no, because a senior guardrails/safety engineer is not naturally a senior experiment-tracking/evaluation engineer.",
            "similarity": 0.5761805519276036
          },
          {
            "a_dim_id": "guardrails-and-safety-controls",
            "a_name": "Guardrails and Safety Controls",
            "a_role": "__skill_focal__",
            "b_dim_id": "guardrails-and-safety-controls",
            "b_name": "Guardrails and Safety Controls",
            "b_role": "AI Engineer",
            "pair_kind": "cross_role",
            "reasoning": "Dim A is LLMOps-focused: runtime safety controls like content moderation, prompt injection defense, output filtering, safety classifiers, jailbreak mitigation, and abuse prevention. Dim B is AI Engineer-focused: fallback behavior, refusal logic, and safe response shaping in production. These overlap in safety intent but differ in daily work and ownership. career-track: no, because a senior LLMOps safety-controls practitioner is not automatically a senior AI Engineer for product response design.",
            "similarity": 0.705788758799777
          }
        ],
        "locked_dimensions": [
          {
            "description": "Production hosting, rollout, and runtime operation of large language models. LLMOps belongs here because it includes the deployment mechanics, scaling, and serving reliability needed to expose LLMs as usable services.",
            "exemplar_skills": [
              "LLMOps",
              "LLM serving",
              "model deployment",
              "inference scaling",
              "canary releases for models",
              "GPU inference serving",
              "request batching",
              "serving observability"
            ],
            "in_scope": "LLMOps, model serving endpoints, inference scaling, model rollout, canary deployment, autoscaling, latency optimization, batching, GPU-backed serving, request routing, versioned model releases",
            "name": "LLM Serving and Deployment",
            "out_of_scope": "Prompt engineering and application UX, retrieval pipeline design, safety policy definition, cloud account setup, and general ML experimentation; those belong to separate dimensions such as RAG, guardrails, cloud platforms, or experiment tracking",
            "overlap_flags": [
              {
                "reason": "LLMOps deployments often run on Kubernetes, but this dimension is about the serving lifecycle rather than cluster scheduling and isolation.",
                "with_dim_id": "kubernetes-for-ml-workloads",
                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
              },
              {
                "reason": "Operational LLM workflows may be orchestrated, but pipeline scheduling and retries are owned by the orchestration dimension.",
                "with_dim_id": "workflow-orchestration-for-ml-pipelines",
                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
              },
              {
                "reason": "Production LLM services need monitoring and alerting, but generic platform observability is broader than LLM-specific serving concerns.",
                "with_dim_id": "observability-and-operations",
                "with_dim_name": null,
                "with_role": "Cloud Architect"
              }
            ],
            "tentative_id": "llm-serving-deployment"
          },
          {
            "description": "Runtime controls that constrain LLM behavior, reduce harmful outputs, and enforce product policy. LLMOps often includes these controls because operating LLMs in production requires safety filters, policy checks, and abuse mitigation.",
            "exemplar_skills": [
              "LLMOps",
              "content moderation",
              "prompt injection defense",
              "output filtering",
              "safety classifiers",
              "policy enforcement",
              "jailbreak mitigation",
              "abuse prevention"
            ],
            "in_scope": "LLMOps, content filtering, prompt injection defenses, output moderation, policy enforcement, jailbreak detection, refusal behavior, safety classifiers, toxicity filtering, rate limiting for abuse",
            "name": "Guardrails and Safety Controls",
            "out_of_scope": "Model training, retrieval architecture, deployment scaling, and governance documentation; those belong to training/serving, RAG, or AI governance dimensions",
            "overlap_flags": [
              {
                "reason": "Both address model risk and controls, but this dimension is focused on runtime safeguards while governance covers auditability and compliance.",
                "with_dim_id": "ai-governance-and-model-security",
                "with_dim_name": null,
                "with_role": "AI Engineer, ML Engineer, ML Ops Engineer"
              },
              {
                "reason": "Some safety patterns inspect retrieved context, but retrieval and grounding are primarily owned by the RAG dimension.",
                "with_dim_id": "rag-architectures",
                "with_dim_name": null,
                "with_role": "AI Engineer"
              }
            ],
            "tentative_id": "guardrails-and-safety-controls"
          },
          {
            "description": "Measurement, comparison, and validation of model and prompt changes before and after release. LLMOps belongs here when it refers to evaluating prompts, models, and system behavior with repeatable metrics and test sets.",
            "exemplar_skills": [
              "LLMOps",
              "prompt evaluation",
              "offline evaluation",
              "A/B testing",
              "model comparison",
              "quality metrics",
              "human-in-the-loop evaluation",
              "regression test suites"
            ],
            "in_scope": "LLMOps, prompt evaluation, offline evals, A/B testing for prompts, regression test sets, model comparison, quality metrics, human evaluation, experiment logging, reproducibility, benchmark harnesses",
            "name": "Experiment Tracking and Evaluation",
            "out_of_scope": "Serving infrastructure, safety policy enforcement, and retrieval system design; those are handled by deployment, guardrails, or RAG dimensions",
            "overlap_flags": [
              {
                "reason": "Evaluation jobs are often scheduled in pipelines, but orchestration owns the execution flow rather than the evaluation methodology.",
                "with_dim_id": "workflow-orchestration-for-ml-pipelines",
                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
              },
              {
                "reason": "Evaluation code may use ML libraries, but the dimension is about measurement practices rather than model implementation APIs.",
                "with_dim_id": "ml-frameworks-and-libraries",
                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
              }
            ],
            "tentative_id": "experiment-tracking-and-evaluation"
          },
          {
            "description": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.",
            "exemplar_skills": [
              "Experiment Tracking and Evaluation"
            ],
            "in_scope": "Skills, tools, and practices that belong under Experiment Tracking and Evaluation for the target role, including items implied by the dimension rationale.",
            "name": "Experiment Tracking and Evaluation",
            "out_of_scope": "Adjacent clusters explicitly not owned by Experiment Tracking and Evaluation, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "experiment-tracking-and-evaluation"
          },
          {
            "description": "Tools and frameworks for hosting and serving LLM models in production environments.",
            "exemplar_skills": [
              "LLM Serving \u0026 Deployment"
            ],
            "in_scope": "Skills, tools, and practices that belong under LLM Serving \u0026 Deployment for the target role, including items implied by the dimension rationale.",
            "name": "LLM Serving \u0026 Deployment",
            "out_of_scope": "Adjacent clusters explicitly not owned by LLM Serving \u0026 Deployment, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "llm-serving-deployment"
          },
          {
            "description": "Runtime controls that constrain model behavior, block unsafe outputs, and enforce product policy. This is a core AI Engineer responsibility because the role owns fallback behavior, refusal logic, and safe response shaping in production.",
            "exemplar_skills": [
              "Guardrails and Safety Controls"
            ],
            "in_scope": "Skills, tools, and practices that belong under Guardrails and Safety Controls for the target role, including items implied by the dimension rationale.",
            "name": "Guardrails and Safety Controls",
            "out_of_scope": "Adjacent clusters explicitly not owned by Guardrails and Safety Controls, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "guardrails-and-safety-controls"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "LLMOps",
          "placement_confidence": 0.92,
          "primary_dimension": "llm-serving-deployment",
          "reasoning": "Deterministic JD placement: locked_dimensions has 6 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [
            "guardrails-and-safety-controls",
            "experiment-tracking-and-evaluation"
          ],
          "skill_id": "llmops"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "mlflow",
            "mlflow-tracking",
            "metrics",
            "logging",
            "vllm",
            "bentoml",
            "lakefs",
            "llamaindex"
          ],
          "requires": [],
          "skill_id": "llmops",
          "suppress_on_match": []
        },
        "skill_id": "llmops",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.93,
          "name": "LLMOps",
          "reasoning": "LLMOps is fundamentally a way of working for operating, deploying, and monitoring LLM systems, so by the Concept vs Methodology rule it fits Methodology rather than a tool or platform.",
          "skill_id": "llmops",
          "subtype": "llm_operations_methodology",
          "type": "Methodology"
        },
        "warnings": [
          "stage3_post_filter_dropped_catalog_only_locked_dims:43-\u003e6"
        ]
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Experiment Tracking and Evaluation",
            "id": 44,
            "rationale": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.",
            "slug": "experiment-tracking-and-evaluation",
            "source": "db"
          },
          "input_skill": "Evaluation Frameworks",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Ops Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Experiment Tracking and Evaluation",
            "id": 44,
            "rationale": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.",
            "slug": "experiment-tracking-and-evaluation",
            "source": "db"
          },
          "input_skill": "Evaluation Frameworks",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Ops Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Evaluation Frameworks",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Framework",
          "skill_nature": "FRAMEWORK",
          "sub_category": "evaluation_framework",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "EMERGING"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "\u201cEvaluation Frameworks\u201d is a specific, standard ML/experimentation concept; unlikely to be confused with other distinct catalog skills."
          },
          "context_keywords": {
            "context_keywords": [
              "metrics",
              "A/B testing",
              "performance indicators",
              "data analysis",
              "feedback loops",
              "user research",
              "statistical significance",
              "experimental design",
              "control groups",
              "hypothesis testing",
              "analytics tools",
              "iteration",
              "qualitative assessment",
              "quantitative analysis",
              "reporting frameworks"
            ]
          },
          "maturity": {
            "confidence": 0.78,
            "maturity": "emerging",
            "reasoning": "Appears increasingly in ML/LLM job descriptions and vendor docs, but there\u2019s no single dominant standard yet; adoption is growing around tools like LangSmith, Ragas, and OpenAI Evals."
          },
          "skill_id": "evaluation-frameworks",
          "vendor_license": {
            "confidence": 0.5,
            "license": null,
            "vendor": null,
            "year_introduced": null
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [
          {
            "a_dim_id": "experiment-tracking-and-evaluation",
            "a_name": "Experiment Tracking and Evaluation",
            "a_role": "__skill_focal__",
            "b_dim_id": "experiment-tracking-and-evaluation",
            "b_name": "Experiment Tracking and Evaluation",
            "b_role": "ML Ops Engineer",
            "pair_kind": "cross_role",
            "reasoning": "Cross-role false positive: both names match, but A is model-evaluation focused (Evaluation Frameworks, benchmark design, metric selection, offline model evaluation, run comparison), while B is ML Ops oriented around reproducibility, metrics, artifacts, and offline evaluation workflows. career-track: no, because a senior practitioner in model evaluation/benchmarking is not naturally a senior ML Ops engineer managing experiment artifacts and operational workflows.",
            "similarity": 0.8748604112115383
          }
        ],
        "locked_dimensions": [
          {
            "description": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This skill belongs here because evaluation frameworks define how results are measured, benchmarked, and judged across experiments.",
            "exemplar_skills": [
              "Evaluation Frameworks",
              "experiment tracking",
              "offline model evaluation",
              "benchmark design",
              "metric selection",
              "run comparison"
            ],
            "in_scope": "Evaluation Frameworks, experiment tracking, offline evaluation, benchmark suites, metric definitions, run comparison, model selection criteria, reproducibility checks, validation datasets",
            "name": "Experiment Tracking and Evaluation",
            "out_of_scope": "Training loop implementation, model architecture design, deployment serving stacks, production monitoring, governance and compliance controls",
            "overlap_flags": [
              {
                "reason": "Framework APIs often include evaluation helpers, but this dimension is about the evaluation process and criteria rather than model-building libraries.",
                "with_dim_id": "ml-frameworks-and-libraries",
                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
              },
              {
                "reason": "Evaluation can be a pipeline step, but orchestration owns scheduling and dependencies, not the evaluation methodology itself.",
                "with_dim_id": "workflow-orchestration-for-ml-pipelines",
                "with_dim_name": null,
                "with_role": "ML Engineer, ML Ops Engineer"
              }
            ],
            "tentative_id": "experiment-tracking-and-evaluation"
          },
          {
            "description": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.",
            "exemplar_skills": [
              "Experiment Tracking and Evaluation"
            ],
            "in_scope": "Skills, tools, and practices that belong under Experiment Tracking and Evaluation for the target role, including items implied by the dimension rationale.",
            "name": "Experiment Tracking and Evaluation",
            "out_of_scope": "Adjacent clusters explicitly not owned by Experiment Tracking and Evaluation, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "experiment-tracking-and-evaluation"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "Evaluation Frameworks",
          "placement_confidence": 0.92,
          "primary_dimension": "experiment-tracking-and-evaluation",
          "reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "evaluation-frameworks"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [
            "evaluation",
            "metrics"
          ],
          "related_to": [
            "a-b-testing",
            "golden-tests",
            "failure-analysis",
            "model-cards",
            "weights-biases",
            "observability",
            "eu-ai-act-readiness",
            "vision-language-models"
          ],
          "requires": [],
          "skill_id": "evaluation-frameworks",
          "suppress_on_match": []
        },
        "skill_id": "evaluation-frameworks",
        "split_log": [],
        "typed": {
          "alternatives_considered": [
            "Tool: ruled out \u2014 the plural name suggests a reusable software structure rather than an operator-run application.",
            "Concept: ruled out \u2014 this is more than a knowledge unit because it implies an implementable software scaffold."
          ],
          "confidence": 0.78,
          "name": "Evaluation Frameworks",
          "reasoning": "By the Tool vs Framework rule, this refers to a structured codebase or set of abstractions used to build and run evaluations inside, rather than a standalone user-operated tool.",
          "skill_id": "evaluation-frameworks",
          "subtype": "evaluation_framework",
          "type": "Framework"
        },
        "warnings": [
          "stage3_post_filter_dropped_catalog_only_locked_dims:41-\u003e2"
        ]
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Distributed Systems",
          "alias_type": "CANONICAL",
          "id": 2028,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Distributed Systems",
        "id": 1369,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "distributed-systems",
        "sub_category_id": 1035,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Platforms",
            "id": 20,
            "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
            "slug": "cloud-platforms",
            "source": "db"
          },
          "input_skill": "Distributed Systems",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Cybersecurity Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            },
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            },
            {
              "display_name": "Full Stack Engineer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Ops Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Performance and Scalability Tuning",
            "id": 11,
            "rationale": "Techniques for improving throughput, latency, and resource efficiency in backend services. Focuses on profiling, concurrency, load behavior, and bottleneck elimination.",
            "slug": "performance-and-scalability-tuning",
            "source": "db"
          },
          "input_skill": "Distributed Systems",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Distributed Systems",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Distributed Systems",
      "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": [
    "Claude API",
    "Gemini API",
    "LLMOps",
    "Evaluation Frameworks"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Full Stack Engineer",
    "id": 15,
    "rationale": "The role of Full Stack Engineer covers all key primary skills from Node.js to React and TypeScript.",
    "role_archetype": null,
    "slug": "full-stack-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Node.js",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "FastAPI",
      "tag": "in_db"
    },
    {
      "skill": "TypeScript",
      "tag": "in_db"
    },
    {
      "skill": "React",
      "tag": "in_db"
    },
    {
      "skill": "Next.js",
      "tag": "in_db"
    },
    {
      "skill": "LangChain",
      "tag": "in_db"
    },
    {
      "skill": "LlamaIndex",
      "tag": "in_db"
    },
    {
      "skill": "OpenAI API",
      "tag": "in_db"
    },
    {
      "skill": "Claude API",
      "tag": "new"
    },
    {
      "skill": "Gemini API",
      "tag": "new"
    },
    {
      "skill": "RAG",
      "tag": "in_db"
    },
    {
      "skill": "Pinecone",
      "tag": "in_db"
    },
    {
      "skill": "Weaviate",
      "tag": "in_db"
    },
    {
      "skill": "LLMOps",
      "tag": "new"
    },
    {
      "skill": "Evaluation Frameworks",
      "tag": "new"
    },
    {
      "skill": "Distributed Systems",
      "tag": "in_db"
    }
  ],
  "persistence": {
    "items": [
      {
        "chosen_role_id": 15,
        "dimension": {
          "difficulty_hint": "well_known",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 242,
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      {
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        "dimension_id": 198,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
          {
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        ],
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      {
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        ],
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        "roles_from_db": [],
        "skill_dimension_saved": true,
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        },
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        "roles_from_db": [
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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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