AI/ Machine Learning Engineer

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

3–5 years in software engineering and architecture.

At least 2 years designing and deploying ML or LLM-based systems, including 6–12 months on LLM-specific work.

You architect and own complex, high-stakes systems that orchestrate multiple components — and you can point to the design docs and architectural decisions you wrote to get there. You've owned something from architecture through production reliability, not just to launch.

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.

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.

Strong Python. FastAPI or equivalent for production services.

Experience with large datasets, ML pipelines, and distributed systems (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.

Excellent problem-solving, communication, and collaboration.