Engineering Manager
3 weeks ago
Why this role We’re building enterprise‑gradeAgentic 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 operatedLLM‑poweredor ML products at scale (user‑facing or enterprise workflows). Strong coding inPython, JavaandTypeScript/Node ; solid system design and API fundamentals. Exposure tofrontendtechnologies like React, Angular, Flutter Experience on SQL databases like Postgres, MariaDB PracticalMLOps : 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 withDevOps : GitHub Actions, Docker, basic Kubernetes, IaC (Terraform), and one major cloud (GCP/AWS/Azure). Familiar withAI 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 ofmulti‑agentframeworks, graph planners, or workflow engines (Prefect/Temporal). Experience withprivacy‑preserving ML , tenancy isolation, regionalization.
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