Lead Applied Ai Engineer

1 week ago

panipat, India Taggd Full time

Why this roleWe'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/upliftmodeling for experiments.- HRTech exposure (ATS/CRM, interview orchestration, assessments).

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