Engineering Manager - Agentic AI

Engineering Manager - Agentic AI

Location:

Function:

Reports to:

Team size:

Why this role

Agentic AI platform & applications for recruitment

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.