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Pipeline run

55486d12-1a2a-4ac3-a261-1a77e20b0c66

Pipeline LLM cost (USD)
API 1: $0.0048 API 2: $0.0004 API 3: $0.0000 Total: $0.0052

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · AI engineering / LLM systems
Build and ship production AI systems in Python/TypeScript: LLM and prompt pipelines, RAG/retrieval, moderation/classification, evaluation tooling, and backend integrations for live customer deployments.
""real production-grade AI pipelines""
Tech stack maturity
Modern Cloud Native
The skill set centers on AI engineering with embeddings, prompt engineering, RAG, Python, and TypeScript, which strongly aligns with modern cloud-native application development and contemporary AI system design.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
4.70 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
On AI team
"You will work closely with Ajay (Founder, Jetty AI) on architecture decisions."
Builds AI products
"You will help design and implement: LLM pipelines"
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2): LangChain, LlamaIndex, OpenAI API, Anthropic API, Qdrant
Models / concepts (×3): Anthropic, OpenAI, RAG, embeddings, LLM, LLMs, prompt engineering, AI
Evidence — skills matched in JD (23)
Python TypeScript LLM APIs RAG Embeddings Prompt Engineering Text Classification Model Orchestration Retrieval Pipelines Evaluation Datasets Prompt Versioning Cost Monitoring Model Switching Logic LangChain LlamaIndex FastAPI Next.js Supabase PostgreSQL Qdrant OpenAI API Anthropic API Vector Databases
Skill cluster (10 dimension groups, role-scoped)
LLM Operations and Orchestration
LangChain LlamaIndex
LLM Provider APIs
OpenAI API Anthropic API
JavaScript and TypeScript
TypeScript
ML Frameworks and Libraries
Embeddings
Meta-Frameworks & SSR
Next.js
Python Programming
Python
Relational Database Usage
PostgreSQL
Vector Databases
Qdrant
Web Application Frameworks
FastAPI
Cross-cutting / unaligned
LLM APIs RAG Prompt Engineering Text Classification Model Orchestration Retrieval Pipelines Evaluation Datasets Prompt Versioning Cost Monitoring Model Switching Logic Supabase Vector Databases
Show KRA description ↓
We are hiring an AI Engineer / AI Systems Lead who can take ownership of building real production-grade AI pipelines. This is not a research role. This is a builder role for someone who enjoys turning ideas into working systems quickly. You will work across: LLM pipelines RAG architectures moderation systems embeddings workflows evaluation datasets experimentation infrastructure backend integrations You will help design and implement: LLM pipelines Prompt workflows routing logic model orchestration RAG systems vector stores chunking strategies retrieval pipelines grounded reasoning layers Moderation / Trust systems text classification pipelines policy reasoning layers conversation analysis tools AI infrastructure tooling evaluation datasets prompt versioning cost monitoring model switching logic You will work closely with Ajay (Founder, Jetty AI) on architecture decisions. We are looking for someone with: 4 to 10+ years software engineering experience strong Python or Typescript backend experience hands-on experience working with LLM APIs experience building production systems (not notebooks only) Bonus if you have worked with: LangChain LlamaIndex vector databases FastAPI Next.js Supabase Postgres Qdrant OpenAI / Anthropic APIs Strong system thinking matters more than tool familiarity. You will work directly on: real customer deployments real production pipelines real AI architecture decisions not internal experimentation projects This role grows quickly into: AI Technical Lead Architecture Owner Client-facing AI specialist depending on performance. Month 1 is a structured mutual fit evaluation period. You will: work on live pipelines contribute to architecture tasks build evaluation datasets help prototype AI workflows This period is fully paid. At the end of month 1: role converts into long-term engagement based on performance and alignment. You will work within a layered engineering structure: Founder-led architecture senior advisors with 15–20 years experience execution engineers intern research support This allows rapid shipping with strong technical direction. Engineers who: like building fast like working directly with founders want exposure to real AI deployments enjoy solving messy real-world problems This is not suited for candidates looking only for structured corporate roles.

Signals

Skill full-stack-engineer
0.15
Alias ai-engineer
1.00
KRA engineering-manager
0.47

Post-classification

Centroidupdated · n=42
Alias collision log
New-role queue
New skills captured10
New KRA captured

Captured for admin review

LLM APIs primary AI Engineer pending
Supabase AI Engineer pending
Vector Databases AI Engineer pending
Text Classification primary AI Engineer pending
Model Orchestration primary AI Engineer pending
Retrieval Pipelines primary AI Engineer pending
Evaluation Datasets primary AI Engineer pending
Prompt Versioning primary AI Engineer pending
Cost Monitoring primary AI Engineer pending
Model Switching Logic primary AI Engineer pending
Status: completed Created: 2026-06-04T17:03:21.128614Z Updated: 2026-06-04T17:04:05.589661Z API 3 duration: 8656 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

AI Engineer

CASE A

slug: ai-engineer · id: 13 · source: db

Exact alias hit on ai-engineer (1.0) — no other alias at this confidence; skill_top full-stack-engineer 0.15 does not contradict

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

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

Job description

AI Engineer / AI Systems Lead
Jetty AI (India)

Full-time | Remote (India)

About Jetty AI
Jetty AI helps startups and mid-market companies operationalize AI faster.

We design and deploy real production AI systems including:

moderation intelligence systems
matching and recommendation engines
RAG pipelines
evaluation frameworks
agent infrastructure
enterprise AI tooling platforms
We are currently working with international product companies building next-generation AI capabilities across trust & safety, behavioral intelligence, and decision systems.

This role will work directly with the founding team on live deployments.

Role Overview
We are hiring an AI Engineer / AI Systems Lead who can take ownership of building real production-grade AI pipelines.

This is not a research role.

This is a builder role for someone who enjoys turning ideas into working systems quickly.

You will work across:

LLM pipelines
RAG architectures
moderation systems
embeddings workflows
evaluation datasets
experimentation infrastructure
backend integrations
Responsibilities
You will help design and implement:

LLM pipelines
Prompt workflows

routing logic

model orchestration

RAG systems
vector stores

chunking strategies

retrieval pipelines

grounded reasoning layers

Moderation / Trust systems
text classification pipelines

policy reasoning layers

conversation analysis tools

AI infrastructure tooling
evaluation datasets

prompt versioning

cost monitoring

model switching logic

You will work closely with Ajay (Founder, Jetty AI) on architecture decisions.

Ideal Candidate Profile
We are looking for someone with:

4 to 10+ years software engineering experience

strong Python or Typescript backend experience

hands-on experience working with LLM APIs

experience building production systems (not notebooks only)

Bonus if you have worked with:

LangChain

LlamaIndex

vector databases

FastAPI

Next.js

Supabase

Postgres

Qdrant

OpenAI / Anthropic APIs

Strong system thinking matters more than tool familiarity.

What Makes This Role Unique
You will work directly on:

real customer deployments

real production pipelines

real AI architecture decisions

not internal experimentation projects

This role grows quickly into:

AI Technical Lead

Architecture Owner

Client-facing AI specialist

depending on performance.

Compensation
Base salary:

₹12 LPA starting

Performance bonus:

50% to 100% additional depending on contribution and project impact

Rapid growth expected as consulting engagements expand.

First Month Structure (Paid Evaluation Period)
Month 1 is a structured mutual fit evaluation period.

You will:

work on live pipelines

contribute to architecture tasks

build evaluation datasets

help prototype AI workflows

This period is fully paid.

At the end of month 1:

role converts into long-term engagement based on performance and alignment.

Team Structure
You will work within a layered engineering structure:

Founder-led architecture

senior advisors with 15–20 years experience

execution engineers

intern research support

This allows rapid shipping with strong technical direction.

Who Should Apply
Engineers who:

like building fast

like working directly with founders

want exposure to real AI deployments

enjoy solving messy real-world problems

This is not suited for candidates looking only for structured corporate roles.

How to Apply
Send:

LinkedIn

GitHub

brief note on projects you built using LLMs on my latest Linkedin posts

Skills from this JD

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

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

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

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

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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: Angular Frontend Developer, Frontend Developer, Ionic Developer, Node.js Backend Developer, React Frontend Developer, React Native Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Sitecore Development Languages Catalog dimension db id 438

    Library dimension (catalog)

    Roles linked in library: Sitecore Dev

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 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)
Sitecore Development Languages
sitecore-development-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LLM APIs Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
LangChain Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LangChain id=240 · langchain

Aliases — catalog

  • LangChain (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Llm Application Framework
Vendor
Harrison Chase
License
mit
Year introduced
2022
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: LangChain appears in many recent AI/LLM job postings and is widely used in app prototypes, but it’s still not a universal hiring staple like React or AWS.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
146
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • LLM Operations and Orchestration Catalog dimension db id 49

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension saved
LlamaIndex Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LlamaIndex id=244 · llamaindex

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
146
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • LLM Operations and Orchestration Catalog dimension db id 49

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension saved
FastAPI Secondary 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)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Next.js Secondary 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)

  • Frontend Frameworks and Libraries Catalog dimension db id 434

    Library dimension (catalog)

    Roles linked in library: Shopify Dev

  • Meta-Frameworks & SSR Catalog dimension db id 130

    Library dimension (catalog)

    Roles linked in library: Frontend Developer, Web Developer

  • UI Frameworks and Rendering Catalog dimension db id 115

    Library dimension (catalog)

    Roles linked in library: Frontend Developer, Fullstack Developer, Fullstack Developer, Hybrid Mobile Developer, Ionic Developer, Web Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Frontend Frameworks and Libraries
frontend-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 skipped (dimension not under chosen role)
Supabase Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Database
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
PostgreSQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PostgreSQL id=16 · postgresql

Aliases — catalog

  • PostgreSQL (CANONICAL) primary
  • PG 13 (VERSION)
  • PG 14 (VERSION)
  • PG 15 (VERSION)
  • PG 16 (VERSION)
  • PostgreSQL 13 (VERSION)
  • PostgreSQL 14 (VERSION)
  • PostgreSQL 15 (VERSION)
  • PostgreSQL 16 (VERSION)
  • Postgres 13 (VERSION)
  • Postgres 14 (VERSION)
  • Postgres 15 (VERSION)
  • Postgres 16 (VERSION)
  • pg10 (VERSION)
  • pg11 (VERSION)
  • pg12 (VERSION)
  • pg13 (VERSION)
  • pg14 (VERSION)
  • pg15 (VERSION)
  • pg16 (VERSION)
  • postgres (VERSION)
  • postgresql 10 (VERSION)
  • postgresql 11 (VERSION)
  • postgresql 12 (VERSION)
  • postgresql 13 (VERSION)
  • postgresql 14 (VERSION)
  • postgresql 15 (VERSION)
  • postgresql 16 (VERSION)
  • postgresql-16 (VERSION)
  • postgresql10 (VERSION)
  • postgresql11 (VERSION)
  • postgresql12 (VERSION)
  • postgresql13 (VERSION)
  • postgresql14 (VERSION)
  • postgresql15 (VERSION)
  • postgresql16 (VERSION)

Context tags (catalog)

ACID EXPLAIN JSONB PL/pgSQL PostGIS SQL VACUUM backup data integrity database migration extensions indexes indexing joins migration partitioning performance tuning pgAdmin query optimization replication schema stored procedures table partitioning transaction transactions triggers views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
PostgreSQL Global Development Group
License
other_open
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Qdrant Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Qdrant id=1243 · qdrant

Aliases — catalog

  • Qdrant (CANONICAL) primary

Context tags (catalog)

API integration cloud-native data indexing data retrieval distributed architecture embedding high-dimensional data machine learning metadata management nearest neighbors performance optimization real-time analytics scalability similarity search vector search

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Vector Database Platform
Vendor
Qdrant
License
apache_2
Year introduced
2020
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Qdrant appears in growing numbers of AI/vector-search job postings and is actively adopted in RAG stacks, but it is still far less common than PostgreSQL or Elasticsearch in JDs.

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 saved
OpenAI API Secondary 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 saved
Anthropic API Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Anthropic API id=1224 · anthropic-api

Aliases — catalog

  • Anthropic API (CANONICAL) primary

Context tags (catalog)

AI safety API integration Claude cloud deployment data privacy ethical AI machine learning model fine-tuning multi-turn dialogue natural language processing performance metrics prompt engineering real-time inference scalability user feedback

Stored enrichment (catalog DB)

Category
Service
Sub-category
Llm Api Service
Vendor
Anthropic
License
unknown
Year introduced
2023
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in growing numbers of AI/LLM job descriptions and vendor docs, but is still far less universal than OpenAI or AWS APIs; GitHub ecosystem activity is rising rather than saturated.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
EMERGING
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 saved
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)
Embeddings Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Embeddings id=1195 · embeddings

Aliases — catalog

  • Embeddings (CANONICAL)

Context tags (catalog)

BERT GloVe contextual embeddings deep learning dimensionality reduction fastText feature extraction natural language processing nearest neighbors semantic similarity sentence embeddings transfer learning transformers vector space word embeddings

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Vector Representation
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Embeddings are a standard ML concept and appear widely in JDs for search, recommendation, and LLM/RAG roles; major vendors like OpenAI, Cohere, and AWS expose embedding APIs, signaling broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
905
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prompt Engineering Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Prompt engineering id=1207 · prompt-engineering

Aliases — catalog

  • Prompt engineering (CANONICAL)

Context tags (catalog)

AI alignment contextual prompts data annotation evaluation metrics feedback loops fine-tuning iterative testing language models model training natural language processing prompt design prompt optimization prompt templates user experience user intent

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Prompt Engineering
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Prompt engineering is increasingly listed in AI/LLM job descriptions and vendor docs, but it’s still not a universal hiring staple like Python or AWS.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
914
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)
Vector Databases Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Switching Logic Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 skipped (dimension not under chosen role)
TypeScript in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TypeScript in_db
Sitecore Development Languages
sitecore-development-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LangChain in_db
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension saved
LlamaIndex in_db
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension saved
FastAPI in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
FastAPI in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Next.js in_db
Frontend Frameworks and Libraries
frontend-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Next.js in_db
Meta-Frameworks & SSR
meta-frameworks-ssr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Next.js in_db
UI Frameworks and Rendering
ui-frameworks-and-rendering
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Qdrant in_db
Vector Databases
vector-databases
Existing dimension (library) · Role↔dimension saved
OpenAI API in_db
LLM Provider APIs
llm-provider-apis
Existing dimension (library) · Role↔dimension saved
Anthropic API in_db
LLM Provider APIs
llm-provider-apis
Existing dimension (library) · Role↔dimension saved
RAG in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embeddings in_db
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embeddings in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embeddings in_db
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prompt Engineering in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed LLM APIs | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Supabase | type=Database subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Vector Databases | type=Databases subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Text Classification | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Model Orchestration | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Retrieval Pipelines | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Evaluation Datasets | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Prompt Versioning | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=SHORT_LIVED
canonical_skill_proposed Cost Monitoring | type=Monitoring Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Switching Logic | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=SHORT_LIVED
canonical_skill_proposed LLM APIs | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Supabase | type=Database subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Vector Databases | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Text Classification | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Model Orchestration | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Retrieval Pipelines | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Evaluation Datasets | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Prompt Versioning | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=SHORT_LIVED
canonical_skill_proposed Cost Monitoring | type=Cloud Platforms subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Switching Logic | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=SHORT_LIVED
nano JD Parser — gpt-4.1-nano click to toggle
RoleAI Engineer / AI Systems Lead
CompanyJetty AI
Experience4 to 10+ years software engineering experience
CTC{'max': None, 'min': 12, 'raw': '₹12 LPA starting', 'period': 'annual', 'currency': 'INR'}
DomainSoftware & SaaS Products
Location India (remote)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Jetty AI helps startups and",
      "last_5_words": "intelligence, and decision systems."
    },
    "text": "Jetty AI helps startups and mid-market companies operationalize AI faster.\n\nWe design and deploy real production AI systems including:\n\nmoderation intelligence systems\nmatching and recommendation engines\nRAG pipelines\nevaluation frameworks\nagent infrastructure\nenterprise AI tooling platforms\nWe are currently working with international product companies building next-generation AI capabilities across trust \u0026 safety, behavioral intelligence, and decision systems.",
    "word_count": 64
  },
  "ai_kras": [
    "You will help design and implement: LLM pipelines",
    "You will work closely with Ajay (Founder, Jetty AI) on architecture decisions."
  ],
  "certifications": [],
  "company_name": "Jetty AI",
  "ctc": {
    "currency": "INR",
    "max": null,
    "min": 12,
    "period": "annual",
    "raw": "\u20b912 LPA starting"
  },
  "domain": {
    "primary": {
      "aliases": [
        "SaaS",
        "AI Products"
      ],
      "domain": "Software \u0026 SaaS Products"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": 10,
    "min": 4,
    "raw": "4 to 10+ years software engineering experience"
  },
  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": "India",
      "state": null,
      "work_mode": "remote"
    }
  ],
  "role": "AI Engineer / AI Systems Lead",
  "role_aliases": [
    "AI Engineer",
    "AI Systems Lead",
    "AI Technical Lead"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Role Overview",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "We are hiring an AI",
        "last_5_words": "backend integrations"
      },
      "text": "We are hiring an AI Engineer / AI Systems Lead who can take ownership of building real production-grade AI pipelines.\n\nThis is not a research role.\n\nThis is a builder role for someone who enjoys turning ideas into working systems quickly.\n\nYou will work across:\n\nLLM pipelines\nRAG architectures\nmoderation systems\nembeddings workflows\nevaluation datasets\nexperimentation infrastructure\nbackend integrations",
      "word_count": 56
    },
    {
      "bullet_count": 0,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "You will help design and",
        "last_5_words": "on architecture decisions."
      },
      "text": "You will help design and implement:\n\nLLM pipelines\nPrompt workflows\n\nrouting logic\n\nmodel orchestration\n\nRAG systems\nvector stores\n\nchunking strategies\n\nretrieval pipelines\n\ngrounded reasoning layers\n\nModeration / Trust systems\ntext classification pipelines\n\npolicy reasoning layers\n\nconversation analysis tools\n\nAI infrastructure tooling\nevaluation datasets\n\nprompt versioning\n\ncost monitoring\n\nmodel switching logic\n\nYou will work closely with Ajay (Founder, Jetty AI) on architecture decisions.",
      "word_count": 114
    },
    {
      "bullet_count": 0,
      "heading": "Ideal Candidate Profile",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "We are looking for someone",
        "last_5_words": "tool familiarity."
      },
      "text": "We are looking for someone with:\n\n4 to 10+ years software engineering experience\n\nstrong Python or Typescript backend experience\n\nhands-on experience working with LLM APIs\n\nexperience building production systems (not notebooks only)\n\nBonus if you have worked with:\n\nLangChain\n\nLlamaIndex\n\nvector databases\n\nFastAPI\n\nNext.js\n\nSupabase\n\nPostgres\n\nQdrant\n\nOpenAI / Anthropic APIs\n\nStrong system thinking matters more than tool familiarity.",
      "word_count": 104
    },
    {
      "bullet_count": 0,
      "heading": "What Makes This Role Unique",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "You will work directly on:",
        "last_5_words": "depending on performance."
      },
      "text": "You will work directly on:\n\nreal customer deployments\n\nreal production pipelines\n\nreal AI architecture decisions\n\nnot internal experimentation projects\n\nThis role grows quickly into:\n\nAI Technical Lead\n\nArchitecture Owner\n\nClient-facing AI specialist\n\ndepending on performance.",
      "word_count": 56
    },
    {
      "bullet_count": 0,
      "heading": "First Month Structure (Paid Evaluation Period)",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Month 1 is a structured",
        "last_5_words": "performance and alignment."
      },
      "text": "Month 1 is a structured mutual fit evaluation period.\n\nYou will:\n\nwork on live pipelines\n\ncontribute to architecture tasks\n\nbuild evaluation datasets\n\nhelp prototype AI workflows\n\nThis period is fully paid.\n\nAt the end of month 1:\n\nrole converts into long-term engagement based on performance and alignment.",
      "word_count": 66
    },
    {
      "bullet_count": 0,
      "heading": "Team Structure",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "You will work within a",
        "last_5_words": "strong technical direction."
      },
      "text": "You will work within a layered engineering structure:\n\nFounder-led architecture\n\nsenior advisors with 15\u201320 years experience\n\nexecution engineers\n\nintern research support\n\nThis allows rapid shipping with strong technical direction.",
      "word_count": 38
    },
    {
      "bullet_count": 0,
      "heading": "Who Should Apply",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Engineers who like building",
        "last_5_words": "structured corporate roles."
      },
      "text": "Engineers who:\n\nlike building fast\n\nlike working directly with founders\n\nwant exposure to real AI deployments\n\nenjoy solving messy real-world problems\n\nThis is not suited for candidates looking only for structured corporate roles.",
      "word_count": 45
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "TypeScript"
    },
    {
      "is_primary": true,
      "skill_name": "LLM APIs"
    },
    {
      "is_primary": false,
      "skill_name": "LangChain"
    },
    {
      "is_primary": false,
      "skill_name": "LlamaIndex"
    },
    {
      "is_primary": false,
      "skill_name": "FastAPI"
    },
    {
      "is_primary": false,
      "skill_name": "Next.js"
    },
    {
      "is_primary": false,
      "skill_name": "Supabase"
    },
    {
      "is_primary": false,
      "skill_name": "PostgreSQL"
    },
    {
      "is_primary": false,
      "skill_name": "Qdrant"
    },
    {
      "is_primary": false,
      "skill_name": "OpenAI API"
    },
    {
      "is_primary": false,
      "skill_name": "Anthropic API"
    },
    {
      "is_primary": true,
      "skill_name": "RAG"
    },
    {
      "is_primary": true,
      "skill_name": "Embeddings"
    },
    {
      "is_primary": true,
      "skill_name": "Prompt Engineering"
    },
    {
      "is_primary": false,
      "skill_name": "Vector Databases"
    },
    {
      "is_primary": true,
      "skill_name": "Text Classification"
    },
    {
      "is_primary": true,
      "skill_name": "Model Orchestration"
    },
    {
      "is_primary": true,
      "skill_name": "Retrieval Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "Evaluation Datasets"
    },
    {
      "is_primary": true,
      "skill_name": "Prompt Versioning"
    },
    {
      "is_primary": true,
      "skill_name": "Cost Monitoring"
    },
    {
      "is_primary": true,
      "skill_name": "Model Switching Logic"
    }
  ],
  "jd_role": {
    "display_name": "AI Engineer / AI Systems Lead",
    "rationale": null,
    "role_aliases": [
      "AI Engineer",
      "AI Systems Lead",
      "AI Technical Lead"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Jetty AI helps startups and",
        "last_5_words": "intelligence, and decision systems."
      },
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}
API 2 — extract-details
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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.",
            "slug": "llm-provider-apis",
            "source": "db"
          },
          "input_skill": "Anthropic API",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Anthropic API",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "RAG",
          "alias_type": "CANONICAL",
          "id": 1830,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "RAG",
        "id": 1194,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "rag",
        "sub_category_id": 904,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "RAG",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "RAG",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Embeddings",
          "alias_type": "CANONICAL",
          "id": 1831,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Embeddings",
        "id": 1195,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "embeddings",
        "sub_category_id": 905,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "ML Frameworks and Libraries",
            "id": 40,
            "rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
            "slug": "ml-frameworks-and-libraries",
            "source": "db"
          },
          "input_skill": "Embeddings",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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": "Embeddings",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Systems Programming",
            "id": 166,
            "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
            "slug": "d_init_02",
            "source": "db"
          },
          "input_skill": "Embeddings",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Embeddings",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Prompt engineering",
          "alias_type": "CANONICAL",
          "id": 1843,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Prompt engineering",
        "id": 1207,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "prompt-engineering",
        "sub_category_id": 914,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Prompt Engineering",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Prompt Engineering",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Vector Databases",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "vector-databases",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Text Classification",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "text-classification",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Model Orchestration",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "model-orchestration",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Retrieval Pipelines",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "retrieval-pipelines",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Evaluation Datasets",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "evaluation-datasets",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Prompt Versioning",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "SHORT_LIVED",
          "version_strategy": "VERSIONED",
          "volatility": "FAST"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "prompt-versioning",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Cost Monitoring",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Cloud Platforms",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "cost-monitoring",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Model Switching Logic",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "SHORT_LIVED",
          "version_strategy": "VERSIONED",
          "volatility": "FAST"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "model-switching-logic",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "LLM APIs",
    "Supabase",
    "Vector Databases",
    "Text Classification",
    "Model Orchestration",
    "Retrieval Pipelines",
    "Evaluation Datasets",
    "Prompt Versioning",
    "Cost Monitoring",
    "Model Switching Logic"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "AI Engineer",
    "id": 13,
    "rationale": "Exact alias hit on ai-engineer (1.0) \u2014 no other alias at this confidence; skill_top full-stack-engineer 0.15 does not contradict",
    "role_archetype": null,
    "slug": "ai-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "TypeScript",
      "tag": "in_db"
    },
    {
      "skill": "LLM APIs",
      "tag": "new"
    },
    {
      "skill": "LangChain",
      "tag": "in_db"
    },
    {
      "skill": "LlamaIndex",
      "tag": "in_db"
    },
    {
      "skill": "FastAPI",
      "tag": "in_db"
    },
    {
      "skill": "Next.js",
      "tag": "in_db"
    },
    {
      "skill": "Supabase",
      "tag": "new"
    },
    {
      "skill": "PostgreSQL",
      "tag": "in_db"
    },
    {
      "skill": "Qdrant",
      "tag": "in_db"
    },
    {
      "skill": "OpenAI API",
      "tag": "in_db"
    },
    {
      "skill": "Anthropic API",
      "tag": "in_db"
    },
    {
      "skill": "RAG",
      "tag": "in_db"
    },
    {
      "skill": "Embeddings",
      "tag": "in_db"
    },
    {
      "skill": "Prompt Engineering",
      "tag": "in_db"
    },
    {
      "skill": "Vector Databases",
      "tag": "new"
    },
    {
      "skill": "Text Classification",
      "tag": "new"
    },
    {
      "skill": "Model Orchestration",
      "tag": "new"
    },
    {
      "skill": "Retrieval Pipelines",
      "tag": "new"
    },
    {
      "skill": "Evaluation Datasets",
      "tag": "new"
    },
    {
      "skill": "Prompt Versioning",
      "tag": "new"
    },
    {
      "skill": "Cost Monitoring",
      "tag": "new"
    },
    {
      "skill": "Model Switching Logic",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 13,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 13,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 13,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 13,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 13,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 13,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
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