Job Description

ABOUT THE ROLE

We are building a new product at the intersection of agentic AI and hard backend engineering. The work runs deep: distributed systems, demanding data problems, and AI engineering that takes large language models beyond demos, into systems you can trust.

As a Senior Backend Developer you will own substantial parts of the backend: hard design problems, production-grade code, and the standards the rest of the team builds on. AI is not a side topic here — it is part of the daily craft, and you will help define how we practice it.

WHAT YOU'LL DO

• Design and deliver major backend features end to end: architecture, implementation, tests, and production rollout.
• Own the hardest problems in our backend and help shape its architecture.
• Lead delivery with junior developers: break features into tasks, review their code, and raise the bar.
• Push our AI engineering practice forward: better agent tooling, better evaluation, better developer workflows.
• Document the designs and decisions that keep the codebase evolvable.

WHAT WE EXPECT FROM YOU

• Own outcomes: when you take a feature, it ships — designed well, tested, and supported in production.
• Drive technical excellence: set, refine, and enforce the standards the rest of the team builds on.
• Multiply the team: mentor junior developers and give feedback that makes people better, not just code.
• Think in abstractions: our codebase is built on a few deep concepts — you extend them without breaking them.
• Make AI a multiplier: use agents and AI tooling deliberately, and help define how the whole team uses them.

WHAT YOU BRING

• Deep modern Java: concurrency with java.util.concurrent, memory and performance awareness, clean API design.
• Real distributed-systems experience: REST/RPC services, replication or consensus, caching, multi-tenancy.
• Strong persistence background: SQL at the level of schema design and query performance, plus at least one NoSQL family (document, key-value, or column).
• Hands-on LLM / agentic AI experience: integrating models into products through tool calling, RAG, or agent orchestration.
• Production discipline: thorough testing, CI, token-based security (JWT/OAuth2), and an instinct for observability.
• A track record of mentoring developers and leading feature delivery.

BONUS POINTS

• Query engines or database internals (e.g. Apache Calcite, jOOQ).
• Consensus and replication (Raft / Apache Ratis) or other distributed coordination.
• Agent ecosystems: MCP, LangChain4j/LangChain, structured agent evaluation.
• Identity and access: OAuth2/OIDC, LDAP, policy engines.
• Python; enough React/TypeScript to be dangerous.

QUALIFICATIONS

• Bachelor degree in Computer Science or Computer Engineering.
• Other degrees considered with a proven track record in software development.