← Back to history

Pipeline run

174bb5ec-e0e5-4ebb-ba00-7aab44e2825b

Pipeline LLM cost (USD)

API 1: $0.0017 API 2: $0.0000 API 3: $0.0000 Total: $0.0017

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: 2026-05-27T17:15:57.813494Z Updated: 2026-05-27T17:15:57.813494Z

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

Position Title:-Junior PLM Developer /Fresher
Open Positions:-10
Salary:- 12,500 for training period and 3 Lacs per annum once the project starts.
Job Location:- Pune\Bangalore
Work Mode:- Work from Home / Office [Depends on Client WFO Policy]
Skill Sets:- Strong Knowledge on CoreJava, J2EE
Qualification:- B.E / B.Tech / M.E / M.Tech / BCA / MCA / B.Sc / M.Sc
Specialization:- CS / IT / IS / ECE / EEE
Year of passing:- 2019,2020,2021,2022
Percentage:- Minimum 60% in all levels [Exemption of 2 to 3 % is given depending upon the profile] 
Any Bond (Yes / No) :- NA
Provisional/Training Period (if any):- 2 to 3 months [May vary depending upon the candidate’s ability to pick up training topics-till he/she is placed in project]
Certificate Submission:- NA
Interview Rounds:- Round 1:- Coding test.
 Round 2:Technical Round.
 Round 3:HR Round.
Interview Mode:- Virtual Mode
Interview Location:- Virtual Mode 
Tentative Interview Date:-Same or next day of receiving the profile.
Immediate joiner.

Library artifacts (this run)

No artifact rows for this run.

nano JD Parser — gpt-4.1-nano click to toggle

JD type fail

Show raw JSON

{
  "JD_type": "fail"
}

API 1 — extract-from-jd click to toggle

{
  "final_skills": [],
  "jd_role": {
    "display_name": "\u2014",
    "rationale": "Stage 1 marked JD_type=fail (unparseable); body has only 0 canonical-skill mentions and 0 tech-marker hits \u2014 insufficient evidence",
    "role_aliases": [],
    "role_archetype": "\u2014",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "fail"
  },
  "rejected": true,
  "rejection_reason": "Stage 1 marked JD_type=fail (unparseable); body has only 0 canonical-skill mentions and 0 tech-marker hits \u2014 insufficient evidence",
  "run_id": null,
  "stage3_signals": null,
  "stage4_decision": null,
  "stage5_updates": null
}

API 2 — extract-details

{}

API 3 — final-role-output

{}