RecruitRank AI
A hybrid candidate-ranking engine built for the Redrob Data & AI Challenge, producing an auditable top-100 shortlist without relying on keyword matching.
The problem
Keyword-matching resume screens reward buzzword-stuffed resumes over real experience, and they give recruiters no way to see why a candidate ranked where they did. The Redrob Data & AI Challenge asked for a ranking system for a Senior AI Engineer role that was both more accurate than keyword matching and fully auditable — every candidate's score had to be explainable, not a black box.
Approach
- Semantic similarity between resume and job description via TF-IDF + TruncatedSVD, with an optional SentenceTransformers backend for higher-fidelity matching.
- Career-evidence detection — explicit signals for retrieval systems, ranking, RAG, and production ML experience, not just years of tenure.
- Skills and experience assessment layered on top of the semantic match.
- Behavioral signals: activity level, response rate, notice period.
- Red-flag penalties for buzzword inflation, weak career history, and suspicious patterns in the resume text.
- Final score: a weighted blend — 22% semantic similarity, 35% career evidence, 16% skills, plus additional experience/title/behavior/location components, minus red-flag penalties.
Why the core output is deterministic, not LLM-guessed
The whole pipeline is deterministic and CPU-only, with no external API calls. Every component of the final score is a named, weighted term you can trace back to source text in the resume — the opposite of an opaque LLM relevance score.
Tech stack
Results
- Weighted 6-factor scoring model instead of keyword matching.
- Deterministic, CPU-only pipeline — no external API dependencies, no per-request cost.
- Produced a ranked, auditable top-100 shortlist for the challenge's Senior AI Engineer role.