Engineering Manager - Agentic AI

Description :

Role : Engineering Manager - Agentic AI

Location : Gurgaon

Function : Engineering

Reports to : CTO

Team size : 7- 8 engineers (startup pod)

Why this role :

Were building enterprise grade Agentic AI platform & applications for recruitment - from sourcing and screening to interview assistance and offer orchestration. Youll lead a small, high leverage team that ships fast, measures rigorously, and scales responsibly.

What youll do :

- Own delivery end to-end : backlog, execution, quality, and timelines for Agentic AI features.

- Be hands on (3050% 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 teamGitHub 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 youve done (must haves) :

- 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, 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 : 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.