Director of Agentic AI Engineering

About the role

Director of AI Engineering Recruits Lab connects specialized life-sciences and technology talent with organizations that need them—through relationships, not volume. You will turn that model into a durable advantage: production AI that finds passive experts, scores fit against culture and science, and shortens searches that used to take a quarter. This role decides whether we stay a high-touch boutique or become the data‑driven standard in biotech, pharma, and technical staffing.

Responsibilities

• Ship a production matching engine within 6 months that ranks passive life-sciences and tech candidates by verified skills, territory, and buyer‑fit, lifting qualified shortlist conversion by 30%.
• Cut median time‑to‑calibrated‑shortlist from 6–10 weeks to under 4 weeks on core searches by automating sourcing maps, named‑account verification, and pipeline hygiene.
• Stand up an evaluation harness (precision, recall, bias, recruiter override rate) and run it on every model release so consultants trust the scores they show clients.
• Own the full stack from data contracts through inference: ingest CRM, outreach, and public signals; deploy retrieval + ranking; instrument latency, cost, and quality in production.
• Hire and lead a small AI/ML engineering team; set the roadmap so every sprint maps to a measurable recruiter or client outcome, not a demo.
• Partner with search consultants to encode “Tri‑State / Midwest / Southeast” style territory and scientific fluency into features, not just keywords.
• Deliver a 12‑month architecture that scales to new verticals (SaaS, oncology, digital pathology) without rewriting the core matching loop.

Qualifications

• Shipped ranking, retrieval, or matching systems used by non‑engineers in production, with documented lifts in conversion or cycle time.
• Led 3–8 engineers building applied ML (embeddings, search, evaluation, LLM orchestration) rather than research‑only work.
• Comfortable encoding domain rules (quota history, named accounts, scientific buyers) into features and guardrails.
• Proven ownership of data quality, evals, cost, and latency for models that affect revenue conversations.
• Can talk to life‑sciences or technical commercial teams without a translator.

Skills

• Production AI for passive expert discovery and fit scoring.
• Retrieval + ranking systems and LLM orchestration.
• Data contracts, CRM/ATS ingestion, outreach and public signal processing.
• Instrumentation of latency, cost, and quality in production.
• Team leadership and roadmap setting tied to recruiter/client outcomes.
• Feature engineering for territory and scientific fluency (e.g., Tri‑State/Midwest/Southeast).
• Design of scalable architectures for new verticals without rewriting core matching loops.
• Experience with cloud, vector search, LLM APIs, and evaluation harnesses (precision, recall, bias, recruiter override rate).