Staff Backend Engineer (Kotlin/Java)
Location: Prague
Department: Product Engineering
Location Type: HYBRID
Employment Type: FULL_TIME
The opportunity
Why this matters for your career
AI is how we build
- Agent native architecture standards: clear API contracts, semantic naming, and well-defined module boundaries that keep AI effective as systems grow.
- A context infrastructure layer with repo versioned guidance that AI tools automatically load, improving the output of Cursor, Claude Code, and Codex simultaneously.
- AI agent workflows for on-call and incident resolution: triage alerts, pull logs, surface relevant history, and suggest remediation.
- Systematic optimization of AI code review to catch correctness, security, and maintainability issues earlier.
What you will do
- Identify and lead high-impact technical initiatives that improve platform scalability, reliability, or developer experience.
- Own complex system design and architectural decisions, collaborating across teams to align technical direction with product needs.
- Unblock your team by simplifying complexity, cutting scope when needed, and delivering pragmatic solutions that accelerate value delivery.
- Proactively tackle tech debt, instability, or bottlenecks that affect multiple teams - and build reusable solutions that serve the whole org.
- Write high-quality Kotlin (or Java) code and establish clear patterns, documentation, and design artifacts others can build on.
- Mentor engineers across levels, supporting their growth through pairing, feedback, and architectural guidance.
- Represent your team in cross-functional initiatives and engineering-wide discussions around architecture, tooling, or quality.
- Improve the reliability, observability, and performance of shared services - and contribute to our incident response practices.
- Make our codebase AI-ready: define clear module boundaries, improve API contracts, add semantic context, and build the structured documentation that makes AI agents more effective across every repo.
- Design and implement agent workflows that go beyond chat: multi-step reasoning, tool use, autonomous task execution, and human-gated checkpoints.
- Run experiments, validate with real users, and iterate based on evidence. We measure learning velocity, not just output.
- Collaborate closely with product managers and designers to shape what we build, not just how we build it. We expect a product mindset, not just technical execution.
- Act as a knowledge multiplier, sharing what you learn across and beyond your team to raise the bar for everyone.
About you
- Have 5+ years of professional engineering experience.
- 7+ years of professional experience building and evolving backend systems at scale, ideally on platform or infrastructure teams.
- Deep technical expertise in system design, distributed architectures, and scalable backend services.
- Strong proficiency in Kotlin or Java; Ruby experience is a plus - but more importantly, you’re language-agnostic and focus on good thinking.
- Proven ability to drive cross-team initiatives and technical strategy from concept through delivery.
- Experience improving developer experience, CI/CD pipelines, or service reliability across teams.
- Strong communication skills and the ability to drive alignment through crisp documentation and design artifacts (e.g. ADRs, RFCs).
- Experience mentoring and elevating engineers by creating systems, standards, and feedback loops that scale.
- A pragmatic, impact-driven mindset - you bias toward action and push for solutions that balance quality, scope, and speed.
- Bonus: Experience working on authentication, permissions, observability, or similar foundational concerns.
- Bonus: Curiosity to explore AI-based tooling for accelerating backend development or automation.
- Strong communication skills and collaborative mindset.
- Curiosity, adaptability, and a proactive, startup-friendly attitude.
- Bring product thinking to engineering work. You can articulate why something matters for users, not just how it works technically.
- Embrace AI as a daily tool in your own workflow. You use AI coding assistants, iterate on prompts, and constantly look for ways to move faster.
- Are curious about what agent native architecture looks like: how to structure codebases, APIs, and documentation so AI agents can operate effectively at scale.
Our tech stack
- Python for ML code
- Celery and Kubernetes for pipeline orchestration
- AWS, Docker, Helm, Kafka, Git, and CI/CD for real time services
- LangGraph for agent orchestration, Braintrust for observability and evaluation
- GraphQL, Postgres, Datadog, Sentry, and Looker
- Business tools: Slack, Jira, Google suite, Zoom, Notion
- Full Tech Stack
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