Principal Agentic AI Engineer

Job Description Job Description Principal AI Engineer

Recruits Lab exists to connect exceptional life sciences and technology talent with organizations that need them-through relationships, not volume. As Principal AI Engineer you will turn that mission into production systems: models and pipelines that find passive specialists, score fit against real hiring outcomes, and give our recruiters an unfair advantage in markets where the best people never apply.

Performance Objectives

• Ship, within 90 days, a production matching engine that ranks passive life-sciences and tech candidates against live searches using structured outcomes (quota, tenure, named-account signal), not keyword overlap.
• Cut time-to-calibrated-shortlist by 40% in two core practices (life sciences commercial and AI/ML technical) by automating sourcing maps, outreach ranking, and due-diligence prompts recruiters actually use.
• Stand up an evaluation loop that ties model decisions to placement quality-90-day retention, ramp, and client NPS-and retrain quarterly on those labels.
• Own end-to-end reliability of LLM and retrieval systems used in live searches: latency, cost, hallucination rate, and auditability for client-facing work.
• Partner with recruiters to productize two high-leverage workflows (territory mapping and technical-bar screening) so they become default process, not side experiments.
• Define and enforce data contracts, privacy, and bias checks for candidate and client data so we can scale AI without breaking trust.

Environment & Resources

You report to the founding team in a 11-50 person, early-stage firm based in Fort Lauderdale, working remotely-first with recruiters who live in the territories they cover. You will have latitude on stack (Python, modern LLM/RAG tooling, vector search, light MLOps), access to real search data and recruiter feedback, and a mandate to ship rather than prototype. Budget follows proven lift in placement speed and quality.

Essential Qualifications

• Proven ownership of production AI/ML systems that changed a business metric (matching, ranking, retrieval, or agentic workflows)-not research-only or demo work.
• Hands-on depth in LLMs, embeddings, evaluation, and cost/latency tradeoffs; you have shipped and operated, not just prompted.
• Ability to translate messy domain knowledge (quota, buyer types, passive talent maps) into features, labels, and guardrails recruiters will trust.
• Comfort working with small teams: you write the design, the code, the eval, and the runbook.
• Track record of ethical handling of people data and clear communication with non-engineers.

Role Selling Points

• Direct line from model to placement: you will see whether a ranking change filled a histology sales seat in Tri-State or a Principal ML role in Boston.
• Domain that compounds-life sciences and specialized tech hiring is relationship-dense, high-stakes, and poorly served by generic ATS AI.
• Early-stage ownership: architecture, priorities, and standards are still yours to set.
• Mission you can defend: candidate-centric staffing, not spray-and-pray volume.

If you want to build AI that actually moves hard-to-fill searches, apply with a short note on a production system you owned and the metric it moved. We hire the same way we recruit: targeted, calibrated, and serious about outcomes.