About the role
You will build the first version of Azisly's Hiring Intelligence models the system that turns recorded interview transcripts into evidence-backed candidate profiles and explainable, ranked shortlists that real recruiters act on. This is an applied ML role, not a research role. The centre of gravity is: LLM-based structured extraction done with engineering discipline, a transparent matching/scoring model, and the evaluation harness that proves (or disproves) that any of it works. You will own this pipeline end to end from raw transcript to number-in database and ship it to production. We operate on one hard rule: LLMs extract evidence; they never make the final prediction.
Responsibilities:
- Build the extraction pipeline: LLM-based structured extraction of competency evidence from interview transcripts, versioned prompts and rubrics, persisted outputs with provenance, evidence quotes, and measured accuracy against expert labels.
- Treated as an engineering system, not prompt tinkering.
- Build the v1 scoring/matching model: a transparent, requirement-weighted scoring model that ranks candidates against role requirements; interpretability is a hard constraint. Lay the groundwork for learning-to-rank as recruiter-preference data accumulates.
- Build the evaluation harness: precision@K against recruiter picks, agreement with expert labels, calibration where used, and subgroup fairness checks on ranked outcomes.
- Every model version ships with an evaluation report.
- Run label operations: design and run the expert-labelling loop with our SMEs and the recruiter preference capture; own training-data snapshots and model versioning.
- Ship to production: own the full lifecycle prototyping, evaluation, deployment, and monitoring working with Python, Postgres, S3 and standard job-queue/batch infrastructure.
- Work independently: take ambiguous problems, figure out the right approach, propose it in writing, and drive it to production with minimal hand-holding.