Pipeline run
0cdbcd3e-0a82-4fa2-a9ee-2fa9a936056e
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
• 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…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Full Stack Engineer
CASE Dslug: 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.
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.
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)
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 |
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
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) |
Aliases — catalog
- FastAPI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Sebastián Ramírez
- License
- mit
- Year introduced
- 2018
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: FastAPI appears in many Python backend job postings and has strong GitHub adoption; it’s now a common choice for API development alongside Flask/Django rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- 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)
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) |
Aliases — catalog
- React (CANONICAL) primary
- React 0.13 (VERSION)
- React 0.14 (VERSION)
- React 15 (VERSION)
- React 15.x (VERSION)
- React 16 (VERSION)
- React 16.x (VERSION)
- React 17 (VERSION)
- React 17.x (VERSION)
- React 18 (VERSION)
- React 18.x (VERSION)
- React 19 (VERSION)
- React v15 (VERSION)
- React v16 (VERSION)
- React v17 (VERSION)
- React v18 (VERSION)
- React v19 (VERSION)
- ReactJS 18 (VERSION)
- react 15 (VERSION)
- react 16 (VERSION)
- react 17 (VERSION)
- react 18 (VERSION)
- react 19 (VERSION)
- react15 (VERSION)
- react16 (VERSION)
- react17 (VERSION)
- react18 (VERSION)
- react19 (VERSION)
- reactjs 18 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Frontend Framework
- Vendor
- Meta
- License
- mit
- Year introduced
- 2013
- Confidence
- 0.98
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 18
Maturity reasoning: React appears in high-volume frontend job postings across startups and enterprises and remains a default hiring-pipeline skill, with strong GitHub/npm usage and ecosystem activity.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 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 |
Aliases — catalog
- Next.js (CANONICAL) primary
- Next 10 (VERSION)
- Next 11 (VERSION)
- Next 12 (VERSION)
- Next 13 (VERSION)
- Next 14 (VERSION)
- Next 15 (VERSION)
- Next 2 (VERSION)
- Next 3 (VERSION)
- Next 4 (VERSION)
- Next 5 (VERSION)
- Next 6 (VERSION)
- Next 7 (VERSION)
- Next 8 (VERSION)
- Next 9 (VERSION)
- Next.js 1 (VERSION)
- Next.js 10 (VERSION)
- Next.js 11 (VERSION)
- Next.js 12 (VERSION)
- Next.js 13 (VERSION)
- Next.js 14 (VERSION)
- Next.js 15 (VERSION)
- Next.js 2 (VERSION)
- Next.js 3 (VERSION)
- Next.js 4 (VERSION)
- Next.js 5 (VERSION)
- Next.js 6 (VERSION)
- Next.js 7 (VERSION)
- Next.js 8 (VERSION)
- Next.js 9 (VERSION)
- next (VERSION)
- next.js (VERSION)
- next.js 14 (VERSION)
- nextjs (VERSION)
- nextjs 14 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Vercel
- License
- mit
- Year introduced
- 2016
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Next.js appears in many frontend/full-stack job descriptions and is a common React meta-framework for production apps; Vercel’s ecosystem and strong GitHub adoption signal broad market demand.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Meta-Frameworks & SSR Catalog dimension db id 130
Library dimension (catalog)
Roles linked in library: Frontend Engineer
-
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 |
Aliases — catalog
- LangChain (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Llm Application Framework
- Vendor
- Harrison Chase
- License
- mit
- Year introduced
- 2022
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LangChain appears in many recent AI/LLM job postings and is widely used in app prototypes, but it’s still not a universal hiring staple like React or AWS.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 146
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Operations and Orchestration Catalog dimension db id 49
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, 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) |
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)
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) |
Aliases — catalog
- OpenAI API (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Llm Api Service
- Vendor
- OpenAI
- License
- other_open
- Year introduced
- 2020
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Strong JD signal: OpenAI API is now commonly listed in AI/ML and full-stack roles, and OpenAI’s own platform docs and ecosystem integrations show broad production adoption.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 1006
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Provider APIs Catalog dimension db id 195
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Provider APIs
llm-provider-apis
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
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.
Anthropic ·unknown ·since 2023 (0.85)
“Claude API” is a specific vendor/product name (Anthropic) and is unlikely to be confused with other catalog services.
Not versioned
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.
- 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 |
Skill enrichment (orchestrator / LLM)
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.
Gemini ·unknown ·since 2023 (0.80)
“Gemini API” is a specific Google/Google AI model API name; unlikely to be confused with other distinct model APIs in typical JDs.
Not versioned
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.
- 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) |
Aliases — catalog
- RAG (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Retrieval Augmented Generation
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: RAG appears in many recent AI/ML job descriptions and vendor docs, but it is still not a universal baseline skill like Python or SQL; market demand is rising fast rather than fully standardized.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 904
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Pinecone (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Vector Database Platform
- Vendor
- Pinecone
- License
- unknown
- Year introduced
- 2021
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Pinecone appears in a growing number of AI/vector-search job postings and vendor docs, but it is still far from universal compared with PostgreSQL or AWS.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 177
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Operations and Orchestration Catalog dimension db id 49
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, 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) |
Aliases — catalog
- Weaviate (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
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.
(0.95)
LLMOps is a specific term for LLM deployment/operations; it’s unlikely to be confused with other catalog skills.
Not versioned
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.
- 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) |
Skill enrichment (orchestrator / LLM)
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.
(0.50)
“Evaluation Frameworks” is a specific, standard ML/experimentation concept; unlikely to be confused with other distinct catalog skills.
Not versioned
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.
- 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) |
Aliases — catalog
- Distributed Systems (CANONICAL)
Context tags (catalog)
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
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": {
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"SaaS",
"Product Companies"
],
"domain": "Software \u0026 SaaS Products"
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"Machine Learning"
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"domain": "AI/ML"
}
},
"education": [],
"experience": {
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"raw": "6\u201310 Years"
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"job_locations": [
{
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],
"city": "Bengaluru",
"country": "India",
"state": null,
"work_mode": "onsite"
}
],
"role": "Staff Engineer / SDE-3 (Backend/Full Stack)",
"role_aliases": [
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"SDE-3",
"Senior Software Engineer"
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{
"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,
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},
{
"is_primary": true,
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}
],
"jd_role": {
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"SDE-3",
"Senior Software Engineer"
],
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},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Pinegap is building AI agents",
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},
"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
},
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},
"education": [],
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"raw": "6\u201310 Years"
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{
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"state": null,
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}
],
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"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
},
{
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"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": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "0cdbcd3e-0a82-4fa2-a9ee-2fa9a936056e",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
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"score": 0.5714,
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},
{
"display_name": "Backend Engineer",
"matched_count": null,
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},
{
"display_name": "Android Engineer",
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"score": 0.5625,
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},
{
"display_name": "ML Engineer",
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},
{
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"score": 0.5,
"slug": "frontend-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
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"matched_count": null,
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},
{
"display_name": "ML Engineer",
"matched_count": null,
"role_id": 3,
"score": 0.367,
"slug": "ml-engineer",
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},
{
"display_name": "Data Engineer",
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"role_id": 2,
"score": 0.3646,
"slug": "data-engineer",
"total_count": null
},
{
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"score": 0.3589,
"slug": "frontend-engineer",
"total_count": null
},
{
"display_name": "Full Stack Engineer",
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"score": 0.3209,
"slug": "full-stack-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Full Stack Engineer",
"matched_count": 6,
"role_id": 15,
"score": 0.3529,
"slug": "full-stack-engineer",
"total_count": 17
},
{
"display_name": "ML Engineer",
"matched_count": 5,
"role_id": 3,
"score": 0.2941,
"slug": "ml-engineer",
"total_count": 17
},
{
"display_name": "ML Ops Engineer",
"matched_count": 5,
"role_id": 16,
"score": 0.2941,
"slug": "ml-ops-engineer",
"total_count": 17
},
{
"display_name": "AI Engineer",
"matched_count": 5,
"role_id": 13,
"score": 0.2941,
"slug": "ai-engineer",
"total_count": 17
},
{
"display_name": "Backend Engineer",
"matched_count": 4,
"role_id": 1,
"score": 0.2353,
"slug": "backend-engineer",
"total_count": 17
}
]
},
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}
API 2 — extract-details
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"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",
"display_name": "Web Application Frameworks",
"id": 2,
"rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
"slug": "web-application-frameworks",
"source": "db"
},
"dimension_id": 2,
"input_skill": "Node.js",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"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": "Full Stack Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1564,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"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": "Full Stack Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "ML Ops Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"dimension_id": 97,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "FastAPI",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1201,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cross-Platform App Languages",
"id": 167,
"rationale": "Languages used to implement shared mobile features across iOS and Android from a common codebase. This is the primary coding surface for hybrid app logic, UI behavior, and platform-specific branching.",
"slug": "cross-platform-app-languages",
"source": "db"
},
"dimension_id": 167,
"input_skill": "TypeScript",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Hybrid Mobile Developer",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "hybrid-mobile-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 524,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "JavaScript and TypeScript",
"id": 114,
"rationale": "Primary implementation languages for browser client code, UI logic, and shared frontend utilities. These languages are the main coding surface for building interactive web experiences in this role.",
"slug": "javascript-and-typescript",
"source": "db"
},
"dimension_id": 114,
"input_skill": "TypeScript",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "frontend-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 524,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "TypeScript",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"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": "Full Stack Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 524,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"dimension_id": 97,
"input_skill": "TypeScript",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 524,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "UI Frameworks and Rendering",
"id": 115,
"rationale": "Component frameworks and rendering models used to build browser screens, reusable UI, and interactive client flows. This is a core cluster because frontend engineers spend much of their time composing and updating view hierarchies.",
"slug": "ui-frameworks-and-rendering",
"source": "db"
},
"dimension_id": 115,
"input_skill": "React",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Hybrid Mobile Developer",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "hybrid-mobile-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 610,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Meta-Frameworks \u0026 SSR",
"id": 130,
"rationale": "Frameworks that build on UI libraries to provide routing, server-side rendering, and static site generation.",
"slug": "meta-frameworks-ssr",
"source": "db"
},
"dimension_id": 130,
"input_skill": "Next.js",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "frontend-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 705,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "UI Frameworks and Rendering",
"id": 115,
"rationale": "Component frameworks and rendering models used to build browser screens, reusable UI, and interactive client flows. This is a core cluster because frontend engineers spend much of their time composing and updating view hierarchies.",
"slug": "ui-frameworks-and-rendering",
"source": "db"
},
"dimension_id": 115,
"input_skill": "Next.js",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Hybrid Mobile Developer",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "hybrid-mobile-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 705,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"dimension_id": 49,
"input_skill": "LangChain",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "ML Ops Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 240,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 15,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"dimension_id": 49,
"input_skill": "LlamaIndex",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "ML Ops Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 244,
"skill_tag": "in_db",
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},
{
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"dimension": {
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"rationale": "Direct integration with hosted model APIs used to generate, classify, extract, and transform content. This is the primary vendor surface for shipping AI features because it determines model choice, request shape, streaming, tool use, and cost/latency tradeoffs.",
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},
"dimension_id": 195,
"input_skill": "OpenAI API",
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"roles_from_db": [
{
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],
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},
{
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},
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"input_skill": "RAG",
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"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1194,
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},
{
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"dimension": {
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},
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{
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{
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{
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],
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},
{
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},
"dimension_id": 198,
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"roles_from_db": [
{
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],
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},
{
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"slug": "vector-databases",
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},
"dimension_id": 198,
"input_skill": "Weaviate",
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"roles_from_db": [
{
"display_name": "AI Engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 1242,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 15,
"dimension": {
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},
"dimension_id": 20,
"input_skill": "Distributed Systems",
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"matched_chosen_role": true,
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{
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{
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{
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{
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{
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{
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{
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],
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"skill_id": 1369,
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{
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},
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"roles_from_db": [
{
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},
{
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{
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},
"dimension_id": 96,
"input_skill": "Claude API",
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"matched_chosen_role": false,
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"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1632,
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{
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"dimension": {
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},
"dimension_id": 127,
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"matched_chosen_role": true,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
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{
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],
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{
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"dimension_id": 96,
"input_skill": "Gemini API",
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"matched_chosen_role": false,
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"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1633,
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},
{
"chosen_role_id": 15,
"dimension": {
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"roles_from_db": [
{
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],
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{
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},
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{
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{
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{
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],
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{
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{
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{
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],
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}
],
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}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.