Principal Software Engineer - LLM/Agent Systems

Role Overview :

We're looking for someone with 6+ years of Software Engineering experience and hands-on experience building LLM-powered or agent-based systems in production.

Technical Skills :

- Principal individual contributor.

- Platform & API; core runtime; logic modernisation.

- India-based role in a global distributed squad; planned overlap with Europe and North America and ownership of outcomes end to end.

- Solid software engineering fundamentals : system design, API design, data modelling, and clean, maintainable code.

- Experience building and running production systems in the cloud (AWS, GCP, or Azure), including containers, Kubernetes or similar, and infrastructure as code.

- Hands-on experience with distributed systems : queues, async workflows, concurrency, reliability, and failure handling.

- Practical experience building with LLMs : tool use, agents, prompt and context engineering, RAG, and evaluation.

- Familiarity with agent protocols and ecosystems (e.g., MCP) and with using AI coding tools day to day.

Leadership & Ownership :

- Comfort working independently and handling ambiguity : you can take a loosely defined problem, scope it, and drive it to production.

- Clear written and verbal communication, including explaining trade-offs to both technical and non-technical audiences.

- At the senior level : we also expect you to lead projects and set technical direction, mentor other engineers, define evaluation and quality standards, and influence architecture across teams.

Mindset & Collaboration :

- Growth mindset : curious, always learning, and open to feedback.

- Works closely with the engineers who use these tools every day; you help the whole organization build software better and faster.

Nice to Have :

- Experience with trace analysis or log mining to find and fix agent failure modes.

- Experience building internal platforms or SDKs that other teams adopted.

- Background in observability, CI/CD, or developer productivity tooling.

- Experience with model routing, cost optimization, or prompt caching.

- Open-source or public contributions in agent tooling or LLM infrastructure.