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Pipeline run

6fe1bb21-f9a5-4a7f-a446-b40713ad5be9

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

SPARSE JD

Nature of work

—

no_db_connection

Tech stack maturity

Mainstream Modern

AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)

0.20 / 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): —

Models / concepts (×3): embeddings, LLM, agentic AI, agentic, AI

Evidence — skills matched in JD (9)

SQL Python Java Spark Databricks Flink AWS GCP Azure

Skill cluster (0 dimension groups, role-scoped)

No dimension groups computed for this JD.

Status: extract_from_jd_done Created: 2026-05-10T13:41:48.746670Z Updated: 2026-05-10T13:41:48.746670Z

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

Experience and Qualifications
• 6+ years of development experience in an enterprise-level engineering environment increasing levels of technical expertise.
• 4+ years of hands-on backend Data Engineering application development experience with an excellent understanding of products with microservice architecture.
• Proven hands-on experience designing, building, and operating data pipelines that enable LLM-based agentic AI systems, including support for embeddings, retrieval layers, and orchestration workflows.
• Expert-level SQL and strong Python proficiency (Java is a plus)
• Experience with distributed processing frameworks (Spark, Databricks, Flink, etc.)
• Experience building data pipelines in cloud-native environments (AWS/GCP/Azure)
• Experience building scalable, fault-tolerant, observable systems
• Good knowledge of Data Structures and Algorithm.
• Strong understanding of data modeling and semantic layer design
• Understanding of embeddings, chunking strategies, retrieval optimization, and re-ranking

Skills from this JD

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

SQL Primary No API 2 row (run stopped after API 1 or history missing)

Python Primary No API 2 row (run stopped after API 1 or history missing)

Java Secondary No API 2 row (run stopped after API 1 or history missing)

Spark Secondary No API 2 row (run stopped after API 1 or history missing)

Databricks Secondary No API 2 row (run stopped after API 1 or history missing)

Flink Secondary No API 2 row (run stopped after API 1 or history missing)

AWS Secondary No API 2 row (run stopped after API 1 or history missing)

GCP Secondary No API 2 row (run stopped after API 1 or history missing)

Azure Secondary No API 2 row (run stopped after API 1 or history missing)

Library artifacts (this run)

No artifact rows for this run.

API 1 — extract-from-jd click to toggle

{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": false,
      "skill_name": "Java"
    },
    {
      "is_primary": false,
      "skill_name": "Spark"
    },
    {
      "is_primary": false,
      "skill_name": "Databricks"
    },
    {
      "is_primary": false,
      "skill_name": "Flink"
    },
    {
      "is_primary": false,
      "skill_name": "AWS"
    },
    {
      "is_primary": false,
      "skill_name": "GCP"
    },
    {
      "is_primary": false,
      "skill_name": "Azure"
    }
  ],
  "run_id": null
}

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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