Lead Agentic AI Engineer - Prompt Engineering

Description :

Why this role :

Were building agentic AI for recruitment workflows - sourcing, screening, interview assistance, and offer orchestration. Youll own LLM/agent design, retrieval, evaluation, safety, and targeted traditional ML models where they outperform or complement LLMs.

What youll 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 wont own K8s/IaC.

What youve done (must-haves) :

- 8 -10 years in software/AI with recent deep focus on LLMs/agentic systems plus delivered traditional ML projects.

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

- Built multi-agent or tool-using systems with LangChain and/or LangGraph (or equivalent), including function/tool calling and planner/executor patterns.

- Delivered RAG end-to-end with vector databases (pgvector/FAISS/Pinecone/Weaviate), hybrid retrieval, and cross-encoder re-ranking.

- Trained and evaluated production ML models using scikit-learn and tree ensembles (XGBoost/LightGBM/CatBoost); tuned via grid/Bayes/Optuna.

- Set up LLM and ML evals (RAGAS/DeepEval/OpenAI Evals or custom), with clear task metrics and online experiments.

- Implemented guardrails & safety and measurable quality gates for both LLM and ML features.

- Product sense : translate use-cases into tasks/metrics; ship iteratively with evidence.

Nice to have

- Re-ranking (bi-encoders/cross-encoders), ColBERT; semantic caching; vector DBs (pgvector/FAISS/Pinecone/Weaviate).

- Light model serving (vLLM/TGI) and adapters (LoRA); PyTorch experience for small finetunes.

- Workflow engines (Temporal/Prefect); basic time-series forecasting; causal inference/uplift modeling for experiments.

- HRTech exposure (ATS/CRM, interview orchestration, assessments).