Engineering Manager - Agentic AI
Location : Gurgaon
Function : Engineering
Reports to : CTO
Team size : 7–8 engineers (startup pod)
Why this role
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.
What you’ll do
- 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; review PRs; unblock tricky problems.
- Design agentic systems : tool‑use orchestration, planning / looping, memory, safety rails, and cost / perf optimization.
- Leverage LLMs smartly : RAG, structured output, function / tool calling, multi‑model routing; evaluate build vs. buy.
- 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; keep prod healthy.
- Security & compliance : enforce data privacy, tenancy isolation, PII handling; partner with Security for audits.
- People leadership : recruit, coach, and grow a high‑trust team; establish rituals (standups, planning, postmortems).
- Stakeholder management : partner with Product / Design / Recruitment SMEs; translate business goals into roadmaps.
What you’ve done (must‑haves)
10+ years in software / ML; 4+ years leading engineers (TL / EM) in high‑velocity product teams.Built and operated LLM‑powered or ML products at scale (user‑facing or enterprise workflows).Strong coding in Python, Java and TypeScript / Node ; solid system design and API fundamentals.Exposure to frontend technologies like React, Angular, FlutterExperience on SQL databases like Postgres, MariaDBPractical 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 engineeringObservability & 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 : GitHub Copilot, Cursor, Claude Code, Code Llama / Codex‑style tools; test automation.Product mindset : measure outcomes (quality, cost, speed), not just outputs; data‑driven decisions.Nice to have
HRTech / recruitment domain (ATS / CRM, assessments, interview orchestration).Retrieval quality tuning, prompt‑engineering at scale, policy / guardrail systems (OpenAI / Guardrails / NeMo Guardrails).Knowledge of multi‑agent frameworks, graph planners, or workflow engines (Prefect / Temporal).Experience with privacy‑preserving ML , tenancy isolation, regionalization.