Lead Applied AI Engineer

Job Description

Lead Applied AI Engineer Location: Gurgaon Function: Engineering (Applied AI) Reports to: CTO Team: 2-3 AI engineers Experience: 8?10 years (majority in Applied AI/LLMs; solid traditional ML) 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) 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).