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Pipeline run

eb39ef1d-7738-4486-a37d-1d4dc9cf82e4

Pipeline LLM cost (USD)
API 1: $0.0035 API 2: $0.0000 API 3: $0.0000 Total: $0.0035

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · AI application development / LLM engineering
Build LLM-powered apps and chat agents over structured data, wiring RAG pipelines with vector stores and LLM frameworks, plus basic MLOps/versioning.
"Design and build AI-powered applications and conversational agents using LLMs to interact with structured data sources"
Tech stack maturity
Modern Cloud Native
The stack centers on current cloud platforms, modern AI/LLM tooling, vector databases, orchestration frameworks, and containerized Python-based workflows, which aligns with a modern cloud-native maturity level.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
✓ Title match
✓ Has AI skill
✓ AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1): —
Frameworks (×2): LangChain, LlamaIndex, Hugging Face, Azure OpenAI, CrewAI, Pinecone
Models / concepts (×3): Anthropic, OpenAI, RAG, LLMs, MLOps, AI
Evidence — skills matched in JD (21)
Python SQL Snowflake Pinecone FAISS ChromaDB LangChain LlamaIndex OpenAI Azure OpenAI Anthropic Hugging Face PostgreSQL MySQL Git Docker MLflow AWS Azure GCP CrewAI
Skill cluster (8 dimension groups, role-scoped)
Cloud Provider Platforms
AWS Azure GCP
ML Frameworks and Libraries
FAISS Hugging Face
Relational Database Usage
PostgreSQL MySQL
Containerization and Image Builds
Docker
LLM Provider APIs
Azure OpenAI
Python Programming
Python
Vector Databases
Pinecone
Cross-cutting / unaligned
SQL Snowflake ChromaDB LangChain LlamaIndex OpenAI Anthropic Git MLflow CrewAI
Show KRA description ↓
- Design and build AI-powered applications and conversational agents using LLMs to interact with structured data sources - Develop RAG pipelines using vector stores (Pinecone, FAISS, ChromaDB) - Integrate frameworks like LangChain, LlamaIndex, CrewAI - Work with OpenAI, Azure OpenAI, Anthropic, Hugging Face APIs - Set up MLOps practices (model versioning, MLflow) Python, SQL, Snowflake, Pinecone, FAISS, ChromaDB, LangChain, LlamaIndex, OpenAI, Azure OpenAI, Anthropic, Hugging Face, PostgreSQL, MySQL, Git, Docker, MLflow, AWS, Azure, GCP, CrewAI

Signals

Skill ml-engineer
0.52
Alias ar-vr-engineer
0.71
KRA ai-compliance-officer
0.47

Post-classification

Centroidupdated · n=1
Alias collision log#5
New-role queue—
New skills captured0
New KRA captured—

v3 pipeline · AI Engineer

approved
0 · charter approved 2026-05-18T22:54:20.263435Z 9.1s
1 · anchor approved 2026-05-18T22:54:30.604828Z 3.5s
2 · dim_gen approved 2026-05-18T22:54:36.159813Z 48.3s
3 · reconciler approved 2026-05-18T22:55:26.814352Z 142.4s
4 · typing pending — —
5 · placement pending — —
6 · containment pending — —
7 · enrichment pending — —
8 · catalog_load approved 2026-05-18T23:23:19.588829Z 106.2s
Status: extract_from_jd_done Created: 2026-05-18T22:54:18.742382Z Updated: 2026-06-21T17:49:31.294226Z
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

No chosen role stored for this run.

Job description

AI Engineer

Join us in building intelligent, AI-driven applications. We are looking for a hands-on AI Engineer with 2-3 years of experience excited about working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and conversational AI systems.

Responsibilities
- Design and build AI-powered applications and conversational agents using LLMs to interact with structured data sources
- Develop RAG pipelines using vector stores (Pinecone, FAISS, ChromaDB)
- Integrate frameworks like LangChain, LlamaIndex, CrewAI
- Work with OpenAI, Azure OpenAI, Anthropic, Hugging Face APIs
- Set up MLOps practices (model versioning, MLflow)

Required skills: Python, SQL, Snowflake, Pinecone, FAISS, ChromaDB, LangChain, LlamaIndex, OpenAI, Azure OpenAI, Anthropic, Hugging Face, PostgreSQL, MySQL, Git, Docker, MLflow, AWS, Azure, GCP, CrewAI

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 No API 2 row (run stopped after API 1 or history missing)
SQL Primary No API 2 row (run stopped after API 1 or history missing)
Snowflake Primary No API 2 row (run stopped after API 1 or history missing)
Pinecone Primary No API 2 row (run stopped after API 1 or history missing)
FAISS Primary No API 2 row (run stopped after API 1 or history missing)
ChromaDB Primary No API 2 row (run stopped after API 1 or history missing)
LangChain Primary No API 2 row (run stopped after API 1 or history missing)
LlamaIndex Primary No API 2 row (run stopped after API 1 or history missing)
OpenAI Primary No API 2 row (run stopped after API 1 or history missing)
Azure OpenAI Primary No API 2 row (run stopped after API 1 or history missing)
Anthropic Primary No API 2 row (run stopped after API 1 or history missing)
Hugging Face Primary No API 2 row (run stopped after API 1 or history missing)
PostgreSQL Primary No API 2 row (run stopped after API 1 or history missing)
MySQL Primary No API 2 row (run stopped after API 1 or history missing)
Git Primary No API 2 row (run stopped after API 1 or history missing)
Docker Primary No API 2 row (run stopped after API 1 or history missing)
MLflow Primary No API 2 row (run stopped after API 1 or history missing)
AWS Primary No API 2 row (run stopped after API 1 or history missing)
Azure Primary No API 2 row (run stopped after API 1 or history missing)
GCP Primary No API 2 row (run stopped after API 1 or history missing)
CrewAI Primary No API 2 row (run stopped after API 1 or history missing)

Library artifacts (this run)

No artifact rows for this run.
nano JD Parser — gpt-4.1-nano click to toggle
RoleAI Engineer
Experience2-3 years of experience
DomainOther
JD type pass
Show raw JSON 
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": 3,
    "min": 2,
    "raw": "2-3 years of experience"
  },
  "job_locations": [],
  "role": "AI Engineer",
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 5,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Responsibilities - Design and build",
        "last_5_words": "practices (model versioning, MLflow)"
      },
      "text": "- Design and build AI-powered applications and conversational agents using LLMs to interact with structured data sources\n- Develop RAG pipelines using vector stores (Pinecone, FAISS, ChromaDB)\n- Integrate frameworks like LangChain, LlamaIndex, CrewAI\n- Work with OpenAI, Azure OpenAI, Anthropic, Hugging Face APIs\n- Set up MLOps practices (model versioning, MLflow)",
      "word_count": 51
    },
    {
      "bullet_count": 0,
      "heading": "Required skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Required skills: Python, SQL,",
        "last_5_words": "Docker, MLflow, AWS, Azure,"
      },
      "text": "Python, SQL, Snowflake, Pinecone, FAISS, ChromaDB, LangChain, LlamaIndex, OpenAI, Azure OpenAI, Anthropic, Hugging Face, PostgreSQL, MySQL, Git, Docker, MLflow, AWS, Azure, GCP, CrewAI",
      "word_count": 30
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle 
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "Pinecone"
    },
    {
      "is_primary": true,
      "skill_name": "FAISS"
    },
    {
      "is_primary": true,
      "skill_name": "ChromaDB"
    },
    {
      "is_primary": true,
      "skill_name": "LangChain"
    },
    {
      "is_primary": true,
      "skill_name": "LlamaIndex"
    },
    {
      "is_primary": true,
      "skill_name": "OpenAI"
    },
    {
      "is_primary": true,
      "skill_name": "Azure OpenAI"
    },
    {
      "is_primary": true,
      "skill_name": "Anthropic"
    },
    {
      "is_primary": true,
      "skill_name": "Hugging Face"
    },
    {
      "is_primary": true,
      "skill_name": "PostgreSQL"
    },
    {
      "is_primary": true,
      "skill_name": "MySQL"
    },
    {
      "is_primary": true,
      "skill_name": "Git"
    },
    {
      "is_primary": true,
      "skill_name": "Docker"
    },
    {
      "is_primary": true,
      "skill_name": "MLflow"
    },
    {
      "is_primary": true,
      "skill_name": "AWS"
    },
    {
      "is_primary": true,
      "skill_name": "Azure"
    },
    {
      "is_primary": true,
      "skill_name": "GCP"
    },
    {
      "is_primary": true,
      "skill_name": "CrewAI"
    }
  ],
  "jd_role": {
    "display_name": "AI Engineer",
    "rationale": null,
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "Other"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": 3,
      "min": 2,
      "raw": "2-3 years of experience"
    },
    "job_locations": [],
    "role": "AI Engineer",
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 5,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Responsibilities - Design and build",
          "last_5_words": "practices (model versioning, MLflow)"
        },
        "text": "- Design and build AI-powered applications and conversational agents using LLMs to interact with structured data sources\n- Develop RAG pipelines using vector stores (Pinecone, FAISS, ChromaDB)\n- Integrate frameworks like LangChain, LlamaIndex, CrewAI\n- Work with OpenAI, Azure OpenAI, Anthropic, Hugging Face APIs\n- Set up MLOps practices (model versioning, MLflow)",
        "word_count": 51
      },
      {
        "bullet_count": 0,
        "heading": "Required skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Required skills: Python, SQL,",
          "last_5_words": "Docker, MLflow, AWS, Azure,"
        },
        "text": "Python, SQL, Snowflake, Pinecone, FAISS, ChromaDB, LangChain, LlamaIndex, OpenAI, Azure OpenAI, Anthropic, Hugging Face, PostgreSQL, MySQL, Git, Docker, MLflow, AWS, Azure, GCP, CrewAI",
        "word_count": 30
      }
    ],
    "urls": []
  },
  "run_id": "eb39ef1d-7738-4486-a37d-1d4dc9cf82e4",
  "stage3_signals": {
    "alias_match_roles": [
      {
        "display_name": "AR/VR Engineer",
        "matched_count": null,
        "role_id": 8,
        "score": 0.7143,
        "slug": "ar-vr-engineer",
        "total_count": null
      },
      {
        "display_name": "Frontend Engineer",
        "matched_count": null,
        "role_id": 7,
        "score": 0.6,
        "slug": "frontend-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.6,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Ios engineer",
        "matched_count": null,
        "role_id": 6,
        "score": 0.5625,
        "slug": "ios-engineer",
        "total_count": null
      },
      {
        "display_name": "Data Engineer",
        "matched_count": null,
        "role_id": 2,
        "score": 0.5294,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "AI Compliance Officer",
        "matched_count": null,
        "role_id": 12,
        "score": 0.4651,
        "slug": "ai-compliance-officer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.4389,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Backend Engineer",
        "matched_count": null,
        "role_id": 1,
        "score": 0.4361,
        "slug": "backend-engineer",
        "total_count": null
      },
      {
        "display_name": "Android Engineer",
        "matched_count": null,
        "role_id": 4,
        "score": 0.413,
        "slug": "android-engineer",
        "total_count": null
      },
      {
        "display_name": "AR/VR Engineer",
        "matched_count": null,
        "role_id": 8,
        "score": 0.409,
        "slug": "ar-vr-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "ML Engineer",
        "matched_count": 11,
        "role_id": 3,
        "score": 0.5238,
        "slug": "ml-engineer",
        "total_count": 21
      },
      {
        "display_name": "Backend Engineer",
        "matched_count": 8,
        "role_id": 1,
        "score": 0.381,
        "slug": "backend-engineer",
        "total_count": 21
      },
      {
        "display_name": "Data Engineer",
        "matched_count": 7,
        "role_id": 2,
        "score": 0.3333,
        "slug": "data-engineer",
        "total_count": 21
      },
      {
        "display_name": "Cybersecurity Engineer",
        "matched_count": 5,
        "role_id": 5,
        "score": 0.2381,
        "slug": "cybersecurity-engineer",
        "total_count": 21
      },
      {
        "display_name": "DevOps Engineer",
        "matched_count": 5,
        "role_id": 10,
        "score": 0.2381,
        "slug": "devops-engineer",
        "total_count": 21
      }
    ],
    "stage35_ran": false
  },
  "stage4_decision": {
    "alias_collision_detected": true,
    "case": "B",
    "chosen_role": {
      "display_name": "AI Compliance Officer",
      "matched_count": null,
      "role_id": 12,
      "score": 0.4651,
      "slug": "ai-compliance-officer",
      "total_count": null
    },
    "confidence": 0.4651,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "queued": false,
    "reasoning": "Stage 1 title \u0027AI Engineer\u0027 not in catalog; KRA top-2 within margin -\u003e classify into nearest neighbor ai-compliance-officer (0.47)"
  },
  "stage5_updates": {
    "centroid_n_after": 1,
    "centroid_updated": true,
    "collision_log_id": 5,
    "new_kra_attached": null,
    "new_skills_attached": [],
    "queue_entry_id": null,
    "v3_pipeline_triggered": true,
    "v3_role_slug": "ai-engineer",
    "v3_run_id": "b55e239c-c8c2-4144-ab0c-c1bfaf56ea06"
  }
}
API 2 — extract-details 
{}
API 3 — final-role-output 
{}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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