Your tasks
As an AI Backend Engineer, you'll build the technical backbone of our decision intelligence platform. You'll develop scalable backend services that make our forecasting and optimization models, agent-based workflows, and semantic data layer productively usable. You work AI-first: Claude Code or comparable CLIs, MCPs, and your own skills are part of your standard toolkit. You think not only in terms of endpoints and databases, but also in terms of customer value, product impact, and adoption, and you want to strengthen the system, not just solve the task at hand.
- You think in terms of impact and have a strong understanding of the product: You measure whether your features are actually being used, observe usage patterns, and refine the next iteration together with Product and FDE.
- You design, build and operate scalable backend services in Python using FastAPI and pydantic, which productively support agent workflows, forecasting and replenishment.
- You design clear APIs between platform, agents, ML models, frontend and customer systems (ERP, BI, planning tools) and ensure robustness and observability.
- You will build our semantic data layer as the basis for precise forecasts and reliable responses from our agents: modeling in PostgreSQL, transformations with dbt, pipelines, caching and retrieval.
- You integrate LLM and agent systems deep into the backend: Tool Use, Function Calling, RAG pipelines, evaluation frameworks for LLM outputs, guardrails and production deployment.
- You work AI-first. Claude Code, AI-supported development and subagents are part of your daily routine, and you continuously expand our internal setup (CLAUDE, Hooks, Skills, Plan/Execute/Review loop, Plugins, Tooling, Docs).
- Together with FDEs and Product, you translate business and customer requirements into viable technical solutions and make pragmatic trade-offs between speed, scalability and maintainability.
- You prototype quickly and closely with the user: You bring new ideas to FDEs and customers in days instead of weeks, independently gather feedback early on, iterate closely to the real workflow, and decide based on real usage what is worth hardening and rolling out.
Your profile
- 2 to 5 years of professional experience as a backend, software, or AI engineer in a product-oriented environment, ideally in a product or SaaS context. Startup experience is welcome.
- Solid backend foundation: Python with FastAPI and Pydantic, clear API design, asynchronous programming, PostgreSQL and Redis, containerization with Docker and experience with CI/CD pipelines.
- Strong product understanding: You don't ask "How do I build this?", but "What are we building here, for whom, and how will we know it's having an impact?". You think in terms of users, adoption, and impact chains, not tickets.
- Practical experience with LLMs and agent systems: Prompt Engineering, Tool Use and Function Calling, RAG, Eval-Frameworks and Production Deployment.
- Experience with cloud infrastructure, ideally AWS, and a good understanding of Kubernetes and Infrastructure-as-Code with Terraform. You don't need to know everything inside and out, but you should be comfortable working with these technologies.
- A high degree of ownership, a love of ambiguity, and the motivation to continuously develop yourself: in backend engineering, agentic engineering, and your product perspective.
- Clear, convincing communication, both technical and non-technical, with little ego.
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