# Job Intelligence Engine
A production job-architecture mapping system behind a compensation benchmarking platform. Maps client job titles to standardized occupation codes with explicit confidence signalling so coverage is never confused with accuracy.
Problem
HR teams struggle with inconsistent job titles across companies and regions. Manual slotting is subjective; automated systems fail on abbreviations, noisy titles, and cross-cultural variations without systematic taxonomy alignment.
Approach
A deterministic, explainable mapping engine built on a unified occupation spine synthesized from multiple public taxonomies. Retrieval and calibrated similarity match client titles to occupation codes, with separate auto-accept, human-review, and abstain signals so the system always returns a valid code while staying honest about confidence. Proprietary work — implementation details available on request.
Impact
- * Owns the core mapping engine behind a live compensation benchmarking platform.
- * Always returns a valid occupation code while separately emitting confidence signals, so coverage is never confused with accuracy.
- * Built on a unified occupation spine aggregating multiple public occupation taxonomies with pay-calibrated grade bands.
- * Full-stack delivery: FastAPI backend, Next.js dashboard, PostgreSQL, Redis, and AWS deployment across dev/staging/production.