Lead Applied AI Engineer

5 days ago


New Delhi, India Taggd Full time

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 targetedtraditional MLmodels where they outperform or complement LLMs.What you’ll do Hands-on AI (70–80%):design & buildagent workflows(tool use, planning/looping, memory, self-critique) usingmulti-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 onLLMs/agentic systemsplus deliveredtraditional MLprojects. StrongPython ; solid stats/ML fundamentals (bias-variance, CV, A/B testing, power, drift). Builtmulti-agentor tool-using systems withLangChainand/orLangGraph(or equivalent), including function/tool calling and planner/executor patterns. DeliveredRAGend-to-end withvector databases( pgvector/FAISS/Pinecone/Weaviate ), hybrid retrieval, and cross-encoderre-ranking . Trained and evaluated production ML models usingscikit-learnand tree ensembles ( XGBoost/LightGBM/CatBoost ); tuned via grid/Bayes/Optuna. Set upLLM and ML evals(RAGAS/DeepEval/OpenAI Evals or custom), with clear task metrics and online experiments. Implementedguardrails & safetyand 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).


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