Engineering Manager - Agentic AI

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.