Senior AI/ Machine Learning Engineer

B.E./B.Tech in Computer Science, Computer Engineering, or a related field.

5–8 years in software engineering and architecture , with at least 3 years designing and deploying ML or LLM-based systems and 12+ months building agentic or LLM-powered systems that reached production .

You have set technical direction others followed. You can point to design docs you wrote, architectural decisions you owned, and at least one system you took from architecture through production reliability and then lived with — including the incidents.

You are a systems thinker. You look across business domains, find the problem that's actually being solved several times over, and abstract it into building blocks the whole platform can stand on. You step one click out from the problem in front of you and interrogate the assumption underneath it — and you can bring other people to that altitude with you.

You are a power user of agentic coding tools — Claude Code, or equivalent agent harnesses — with real intuition for where models are strong, where they fail, and how to tell the difference before it reaches production. You bring engineering discipline to agent-generated work: you review it, you gate it, you are accountable for it. We care that you've hit the failure modes, not that you've installed the CLI.

You are fluent in current agentic engineering practice, not last year's. Context engineering, tool and skill design, subagent patterns, agent memory, evals and LLM-as-judge, structured outputs, prompt caching, RAG as one retrieval technique among several.

You have opinions about evaluation that you can defend. You know why an agent that passes a benchmark can still be unshippable, and you've built the harness that caught it.

Deep Python. FastAPI or equivalent for production services.

Strong distributed-systems fundamentals; experience with large datasets and ML pipelines (Ray, Spark, or equivalent).

Hands-on with PyTorch, Hugging Face, scikit-learn, pandas.

Containerized microservices (Docker, Kubernetes) and CI/CD (Git, Jenkins, Jira).

Humble and adaptable about code and frameworks. LangGraph or comparable orchestration frameworks are useful; none of them are the skill.

You write and argue clearly. Senior work here is as much about bringing people to a decision as reaching it yourself.