Lead Agentic AI Engineer

2 days ago

Gurugram, India Taggd Full time

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 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). (ref:hirist.tech)

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