Pipeline run
55486d12-1a2a-4ac3-a261-1a77e20b0c66
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
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
AI Engineer
CASE Aslug: 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.
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.
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- 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: 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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Aliases — catalog
- LangChain (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Llm Application Framework
- Vendor
- Harrison Chase
- License
- mit
- Year introduced
- 2022
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LangChain appears in many recent AI/LLM job postings and is widely used in app prototypes, but it’s still not a universal hiring staple like React or AWS.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 146
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Operations and Orchestration Catalog dimension db id 49
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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, 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 |
Aliases — catalog
- FastAPI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Sebastián Ramírez
- License
- mit
- Year introduced
- 2018
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: FastAPI appears in many Python backend job postings and has strong GitHub adoption; it’s now a common choice for API development alongside Flask/Django rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- 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)
-
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Database
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- PostgreSQL (CANONICAL) primary
- PG 13 (VERSION)
- PG 14 (VERSION)
- PG 15 (VERSION)
- PG 16 (VERSION)
- PostgreSQL 13 (VERSION)
- PostgreSQL 14 (VERSION)
- PostgreSQL 15 (VERSION)
- PostgreSQL 16 (VERSION)
- Postgres 13 (VERSION)
- Postgres 14 (VERSION)
- Postgres 15 (VERSION)
- Postgres 16 (VERSION)
- pg10 (VERSION)
- pg11 (VERSION)
- pg12 (VERSION)
- pg13 (VERSION)
- pg14 (VERSION)
- pg15 (VERSION)
- pg16 (VERSION)
- postgres (VERSION)
- postgresql 10 (VERSION)
- postgresql 11 (VERSION)
- postgresql 12 (VERSION)
- postgresql 13 (VERSION)
- postgresql 14 (VERSION)
- postgresql 15 (VERSION)
- postgresql 16 (VERSION)
- postgresql-16 (VERSION)
- postgresql10 (VERSION)
- postgresql11 (VERSION)
- postgresql12 (VERSION)
- postgresql13 (VERSION)
- postgresql14 (VERSION)
- postgresql15 (VERSION)
- postgresql16 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- PostgreSQL Global Development Group
- License
- other_open
- Year introduced
- 1996
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Data Modeling Catalog dimension db id 216
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
-
Relational Database Usage Catalog dimension db id 371
Library dimension (catalog)
Roles linked in library: Go Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Data Modeling
relational-data-modeling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Usage
relational-database-usage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Qdrant (CANONICAL) primary
Context tags (catalog)
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 |
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 saved |
Aliases — catalog
- Anthropic API (CANONICAL) primary
Context tags (catalog)
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 |
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
- Embeddings (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Vector Representation
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Embeddings are a standard ML concept and appear widely in JDs for search, recommendation, and LLM/RAG roles; major vendors like OpenAI, Cohere, and AWS expose embedding APIs, signaling broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 905
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Systems Programming Catalog dimension db id 166
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Systems Programming
d_init_02
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Prompt engineering (CANONICAL)
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- 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
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": {
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"aliases": [
"SaaS",
"AI Products"
],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
"education": [],
"experience": {
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"min": 4,
"raw": "4 to 10+ years software engineering experience"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": "remote"
}
],
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"role_aliases": [
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"AI Systems Lead",
"AI Technical Lead"
],
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"roles_and_responsibilities": [
{
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"heading": "Role Overview",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are hiring an AI",
"last_5_words": "backend integrations"
},
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"word_count": 56
},
{
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"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."
},
"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
},
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"experience": {
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"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",
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],
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"heading": "Role Overview",
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"source_marker": {
"first_5_words": "We are hiring an AI",
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},
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"word_count": 56
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{
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"heading": "Responsibilities",
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"first_5_words": "You will help design and",
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},
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},
{
"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."
},
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"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": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "55486d12-1a2a-4ac3-a261-1a77e20b0c66",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 1.0,
"slug": "ai-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Engineering Manager",
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API 2 — extract-details
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"source": "db"
},
"input_skill": "PostgreSQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Relational Database Usage",
"id": 371,
"rationale": "Working effectively with operational relational databases from Go backend services. This includes schema-aware querying, indexing awareness, transactions, and understanding how service code interacts with PostgreSQL or similar systems.",
"slug": "relational-database-usage",
"source": "db"
},
"input_skill": "PostgreSQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "PostgreSQL",
"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": "Qdrant",
"alias_type": "CANONICAL",
"id": 1879,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Qdrant",
"id": 1243,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "qdrant",
"sub_category_id": 177,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vector Databases",
"id": 198,
"rationale": "Specialized storage and indexing systems used to persist embeddings and support similarity search. This is a distinct vendor-family cluster because AI features often depend on a concrete vector store choice and its operational behavior.",
"slug": "vector-databases",
"source": "db"
},
"input_skill": "Qdrant",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
}
],
"input_skill": "Qdrant",
"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": "OpenAI API",
"alias_type": "CANONICAL",
"id": 1859,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "OpenAI API",
"id": 1223,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "openai-api",
"sub_category_id": 1006,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Provider APIs",
"id": 195,
"rationale": "Direct integration with hosted model APIs used to generate, classify, extract, and transform content. This is the primary vendor surface for shipping AI features because it determines model choice, request shape, streaming, tool use, and cost/latency tradeoffs.",
"slug": "llm-provider-apis",
"source": "db"
},
"input_skill": "OpenAI 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": "OpenAI 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": "Anthropic API",
"alias_type": "CANONICAL",
"id": 1860,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "Anthropic API",
"id": 1224,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "anthropic-api",
"sub_category_id": 1006,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Provider APIs",
"id": 195,
"rationale": "Direct integration with hosted model APIs used to generate, classify, extract, and transform content. This is the primary vendor surface for shipping AI features because it determines model choice, request shape, streaming, tool use, and cost/latency tradeoffs.",
"slug": "llm-provider-apis",
"source": "db"
},
"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",
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