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).

build small, high-signal datasets.

bias / fairness checks for ML.

coordinate batch / real-time inference hooks with platform team.

you won’t own K8s / IaC.

What you’ve done (must-haves)

solid stats / ML fundamentals (bias-variance, CV, A / B testing, power, drift).

tuned via grid / Bayes / Optuna.

ship iteratively with evidence.

Nice to have

vector DBs (pgvector / FAISS / Pinecone / Weaviate).

PyTorch experience for small finetunes.

causal inference / upliftmodeling for experiments.