Lead Applied AI Engineer

Why this role

We're building agentic AI for recruitment workflows—sourcing, screening, interview assistance, and offer orchestration. You'll own LLM / agent design, retrieval, evaluation, safety, and targeted traditional ML models where they outperform or complement LLMs.

What you'll do

• Hands-on AI (70–80%) : design & build agent workflows (tool use, planning / looping, memory, self-critique) using multi-agent frameworks (e.g., LangChain , LangGraph ; plus experience with similar ecosystems like AutoGen / CrewAI is a plus).
• Retrieval & context (RAG) : chunking, metadata, hybrid search, query rewriting, reranking, and context compression.
• Traditional ML : design and ship supervised / unsupervised models for ranking, matching, dedup, scoring, and risk / quality signals.
• Feature engineering, leakage control, CV strategy, imbalanced learning, and calibration.
• Model families : Logistic / Linear, Tree ensembles, kNN, SVMs, clustering, basic time-series.
• Evaluation & quality : offline / online evals (goldens, rubrics, A / B), statistical testing, human-in-the-loop; build small, high-signal datasets.
• Safety & governance : guardrails (policy / PII / toxicity), prompt hardening, hallucination containment; bias / fairness checks for ML.
• Cost / perf optimization : model selection / routing, token budgeting, latency tuning, caching, semantic telemetry.
• Light MLOps (in-collab) : experiment tracking, model registry, reproducible training; coordinate batch / real-time inference hooks with platform team.
• Mentorship : guide 2–3 juniors on experiments, code quality, and research synthesis.
• Collaboration : pair with full-stack / infra teams for APIs / deploy; you won't own K8s / IaC.

What you've done (must-haves)

Nice to have

Skills Required

XGBoost, Python