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Pipeline run

77df0113-3a77-4d22-842d-52b2494e299b

Pipeline LLM cost (USD)

API 1: $0.0000 API 2: $0.0000 API 3: $0.0000 Total: $0.0000

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)

0.00 / 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): —

Evidence — skills matched in JD (0)

No skills extracted

Skill cluster (0 dimension groups, role-scoped)

No dimension groups computed for this JD.

Status: extract_from_jd_done Created: 2026-05-27T14:35:44.029338Z Updated: 2026-06-12T17:32:24.397681Z

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

Experienced in Core Java & Spring boot  Design, develop, and implement Kafka-based microservices using Spring Boot.  Build data pipelines for ingesting, processing, and analyzing large-scale data sets.  Optimize Kafka configurations for performance and reliability.  Work with Big Data technologies such as Hadoop, Spark, and NoSQL databases.  Ensure data security, integrity, and compliance with industry standards.  Troubleshoot and resolve issues related to Kafka topics, consumers, and producers.  Monitor system performance and proactively address bottlenecks.  Participate in code reviews and mentor junior developers.  Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.

A day in the life of an Infoscion

• As part of the Infosys delivery team, your primary role would be to interface with the client for quality assurance, issue resolution and ensuring high customer satisfaction.

Library artifacts (this run)

No artifact rows for this run.

API 1 — extract-from-jd click to toggle

{
  "final_skills": [],
  "jd_role": {
    "display_name": "Experienced in Core Java \u0026 Spring boot \uf0d8 Design, develop, and implement Kafka-based microservices using Spring Boot. \uf0d8 Build data pipelines for ingesting, processing, and analyzing large-scale data sets. \uf0d8 Optimize Kafka configurations for performance and reliability. \uf0d8 Work with Big Data technologies such as Hadoop, Spark, and NoSQL databases. \uf0d8 Ensure data security, integrity, and compliance with industry standards. \uf0d8 Troubleshoot and resolve issues related to Kafka topics, consumers, and producers. \uf0d8 Monitor system performance and proactively address bottlenecks. \uf0d8 Participate in code reviews and mentor junior developers. \uf0d8 Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.",
    "rationale": "JD body too sparse: 36 words, 0 tech-marker hits \u2014 needs more detail (\u003e=80 words or \u003e=2 tech markers) for confident classification",
    "role_aliases": [],
    "role_archetype": "Other",
    "slug": ""
  },
  "nano_parsed": null,
  "rejected": true,
  "rejection_reason": "Sparse JD: JD body too sparse: 36 words, 0 tech-marker hits \u2014 needs more detail (\u003e=80 words or \u003e=2 tech markers) for confident classification",
  "run_id": null,
  "stage3_signals": null,
  "stage4_decision": null,
  "stage5_updates": 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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