Engineering Manager

2 weeks ago

Gurugram, India Taggd Full time

Location : GurgaonFunction : EngineeringReports to : CTOTeam 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, 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. (ref:hirist.tech)

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