| name | job-matching-expertise |
| description | Domain expertise for evaluating job-candidate fit for Backend, ML, and AI Engineering roles. Provides rubrics, quality standards, and decision frameworks—no workflow orchestration. |
Skill: Job Matching Expertise
Purpose
This skill encodes domain knowledge for assessing whether a job posting is a good match for a candidate focused on Backend Engineering, Machine Learning, and AI Engineering roles. It provides evaluation criteria, not execution steps.
Target Role Profile
Source: data/templates/full_resume.md
Summary: Backend + ML/AI Engineering; Python-first; experienced with LLM integration, RAG pipelines, and production ML systems.
Job Quality Signals
These signals are secondary. Use them only to decide between Reviewed vs Reject when the JD lacks clear must-have requirements or is too vague to assess. They must not override the hard-gate logic in HR Initial Screen Standard.
Strong signals ✅
- Specific responsibilities + specific stack/tools (not generic “exciting projects”)
- Clear problem space (what is being built, for whom, why it matters)
- Explicit “required/must-have” list (makes screening possible)
- Engineering fundamentals mentioned (testing, code review, docs, observability)
- Reasonable scope (not one person owning 15 unrelated domains)
Weak signals ⚠️
- Vague JD; no stack; no concrete deliverables
- No explicit must-have requirements (hard to screen; bias toward Reviewed)
- Skill laundry list / buzzword bingo without context
- Copy-paste corporate template; no team/product specifics
HR Initial Screen Standard
This standard answers one question: would an HR/recruiter likely filter this candidate out at initial screen, without assuming any upskilling?
Policy Overrides (immediate ❌)
- Company blacklist: Jerry, Alignerr, TATA
- Intern/internship roles
Hard Reject Rules (immediate ❌)
Reject if any of the following is true.
-
Role function mismatch
- Frontend-only (React/Vue/Angular/Next.js; UI work dominates)
- Mobile-only (iOS/Android/React Native)
- Pure DevOps/SRE where infra/oncall dominates and there is no clear backend/app ownership
- BI/Analyst-heavy “data” roles (dashboards/reporting/stakeholder insights as primary output)
-
Core stack mismatch (required-by-JD)
- JD is strongly bound to Java/Spring, .NET, C++ as the core (Python/ML is absent or only “nice-to-have”)
- JD lists must-have technologies or domains that are not credibly evidenced in the resume
-
Seniority / credential hard mismatch
- Staff/Principal/Lead role with clear expectations beyond current level (e.g., 10–12+ years + org-wide architecture + multi-team leadership)
- PhD required (not preferred) and the candidate does not have it
-
Eligibility constraints
- Work authorization, location/onsite, security clearance, or language requirements explicitly marked as required and not met
-
Duplicate
- Same company + title already processed in the current run
Evidence Standard (how to interpret “must-have”)
- Treat JD items labeled must/required as hard gates.
- A requirement is “met” only if the resume contains readable evidence (role/project ownership + relevant responsibilities + outcomes/scale, where possible).
- Count a requirement as met only if the resume shows at least two of: (a) ownership/responsibility, (b) concrete project context, (c) production use, (d) measurable outcomes/scale.
- Familiarity/mentions without the above evidence should be treated as not met at HR screen.
Classification Output (aligned with batch labels)
- Shortlist: passes HR initial screen (no policy override; no hard-reject triggered; all JD required items have resume evidence; seniority/eligibility aligned).
- Reviewed: cannot confidently decide due to missing/vague JD must-haves, or the resume evidence is weak/implicit but plausibly present.
- Reject: any policy override or hard-reject rule triggered, or any JD required item lacks resume evidence.
Mapping note: Shortlist ≈ Pass initial screen, Reviewed ≈ Unclear/Review, Reject ≈ Fail initial screen.
Workflow Output Contract (Required)
When emitting machine-consumable results (for MCP update steps), use this schema per job:
{
"id": 12345,
"status": "shortlist",
"reason": "Meets Python backend + production ML requirements; no hard reject gates triggered."
}
Hard rules:
status must be exactly one of: shortlist, reviewed, reject (lowercase only).reason is mandatory and concise (1-2 sentences), tied to hard gates/evidence standard.- If uncertain, use
reviewed with explicit uncertainty reason; do not force binary decisions.
- Human-readable labels (
Shortlist/Reviewed/Reject) are for explanation only, never for machine fields.
Notes on Equivalents (optional, conservative)
Allow limited equivalence only when the JD does not state a strict tool requirement:
- FastAPI or Flask/Django (Python API frameworks)
- AWS or GCP or Azure when the requirement is stated broadly as “cloud experience” (IaaS/PaaS). Do not treat them as equivalent when the JD requires a specific cloud provider or a specific managed service.
- Managed vector DBs (Pinecone/Weaviate) ↔ other managed vector DBs (e.g., Milvus managed) when the requirement is “vector DB” broadly
- Kubernetes ↔ other container schedulers only if JD is not explicit about K8s
If the JD explicitly says “must have X in production,” do not substitute equivalents.
Batch Evaluation: Cognitive Biases & Calibration
When evaluating multiple jobs in a batch, be aware of these cognitive risks:
Consistency Risks
| Bias | Description |
|---|
| Standards Drift | Getting progressively more lenient after weak postings, or harsher after strong ones |
| Comparison Bias | Judging relative to batch ("better than last 5") instead of absolute rubric |
| Fatigue Shortcuts | Pattern matching on title/company alone; skimming JDs after 30+ jobs |
Distribution Calibration
A healthy batch typically yields:
| Category | Expected Range |
|---|
| Shortlist | 15-25% |
| Reviewed | 20-30% |
| Reject | 45-65% |
Red flags:
- >80% shortlist → likely too lenient
- >80% reject → likely too harsh
- <5% reviewed → forcing binary decisions; embrace uncertainty
