Engineering Manager

1 day ago


Gurgaon, India Taggd Full time

Engineering Manager - Agentic AI Location: Gurgaon Function: Engineering Team size: 7–8 engineers (startup pod) We’re building enterprise‑grade Agentic AI platform & applications for recruitment —from sourcing and screening to interview assistance and offer orchestration. You’ll lead a small, high‑leverage team that ships fast, measures rigorously, and scales responsibly. Own delivery end‑to‑end: backlog, execution, quality, and timelines for Agentic AI features. Be hands‑on (30–50% coding): set the technical bar in Python/TypeScript; Design agentic systems: tool‑use orchestration, planning/looping, memory, safety rails, and cost/perf optimization. RAG, structured output, function/tool calling, multi‑model routing; Ship production ML/LLM workflows: data pipelines, feature stores, vector indexes, retrievers, model registries. MLOps & Observability: automate training/inference CI/CD; monitor quality, drift, toxicity, latency, cost, and usage. EVALs & quality: define task‑level metrics; set up offline/online EVALs (goldens, rubrics, human‑in‑the‑loop) and guardrails. DevOps (T‑shaped): own pragmatic infra with the team—GitHub Actions, containers, IaC, basic K8s; Security & compliance: enforce data privacy, tenancy isolation, PII handling; 10+ years in software/ML; Built and operated LLM‑powered or ML products at scale (user‑facing or enterprise workflows). ~ Strong coding in Python, Java and TypeScript/Node ; Exposure to frontend technologies like React, Angular, Flutter ~ Experience on SQL databases like Postgres, MariaDB ~ Practical MLOps : experiment tracking, model registries, reproducible training, feature/vectors, A/B rollouts. ~ LLM tooling : orchestration (LangChain/LlamaIndex/DSPy), vector DBs (pgvector/FAISS/Pinecone/Weaviate), RAG patterns, context engineering ~ Observability & EVALs : ML/LLM monitoring, LLM eval frameworks (RAGAS/DeepEval/OpenAI Evals), offline+online testing and human review. ~ Comfortable with DevOps : GitHub Actions, Docker, basic Kubernetes, IaC (Terraform), and one major cloud (GCP/AWS/Azure). ~ Familiar with AI SDLC tools : Product mindset: measure outcomes (quality, cost, speed), not just outputs; data‑driven decisions. HRTech/recruitment domain (ATS/CRM, assessments, interview orchestration). Retrieval quality tuning, prompt‑engineering at scale, policy/guardrail systems (OpenAI/Guardrails/NeMo Guardrails). Experience with privacy‑preserving ML , tenancy isolation, regionalization.



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