Senior Python Engineer – On-site, Warsaw

We are looking for a Middle/Senior Backend Engineer who can design, build and own business-critical production systems. You will have real ownership from system design through deployment, working on scalable backend services and AI-native solutions. The team values technical excellence, clear communication, autonomy and practical decision-making. Engineers use modern AI tools such as Codex and Claude Code to improve development workflows and engineering efficiency.
Requirements:
Backend Experience: Strong senior-level backend engineering experience in production systems. Python: Strong Python skills and the ability to design clean, maintainable backend code. Systems Thinking: Good distributed systems judgment: concurrency, failure handling, data consistency, async work, and service boundaries. Cloud Infrastructure: Hands-on experience with cloud infrastructure such as AWS, GCP, or Azure. Databases: Comfort with database schema design and performance tuning for real-time and high throughput scenarios. Security: A security-conscious approach to engineering: you build for enterprise-grade requirements, handle sensitive data carefully, and think about the security implications of the systems and AI workflows you ship. Ownership: Ability to go from problem statement to design to production rollout with real ownership. Communication: Clear written and verbal communication. You can explain systems, trade-offs, and incidents without hiding behind jargon. AI-Native: AI-native working style. You already use AI tools in your daily engineering workflow, and you're excited to improve how the team builds, not just your own output: better tooling, workflows, and engineering leverage.
Must have: Python, AWS / Azure / GCP, SQL, REST API / Backend Development, Distributed Systems
Nice to have: TypeScript, AI Agents / Agentic AI, LLM / GenAI, RAG, Microservices
Daily tasks: - Design and ship backend services, APIs, data flows, and background processing for production systems.
– Turn vague requirements into concrete technical plans, trade-offs, and execution.
– Own services after launch: reliability, observability, performance, and incident follow-through.
– Instrument what you ship and use production signal to iterate.
– Make sound decisions around system boundaries, data models, interfaces, and scaling paths.
– Work closely with product, frontend, and platform engineers to deliver end-to-end outcomes.
– Improve engineering leverage with AI tooling, automation, and internal workflows.
– Raise the quality bar through code review, design review, and pragmatic technical leadership.