AI Backend Engineer

Responsibilities

• As an AI Backend Engineer, you will build the technical backbone of our Decision Intelligence platform.
• You will develop scalable backend services that operationalize our forecasting and optimization models, agentic workflows, and semantic data layer.
• You work AI‑first: Claude Code or similar CLIs, MCPs, and custom skills are part of your standard toolkit.
• You design, build, and operate scalable backend services in Python using Fast API and pydantic to reliably support agent workflows, forecasting, and replenishment.
• You design clean APIs between the platform, agents, ML models, frontend, and customer systems (ERP, BI, planning tools), ensuring robustness and observability.
• You build and extend our semantic data layer as the foundation for accurate forecasts and precise agent responses: data modeling in Postgre SQL, transformations with dbt, pipelines, caching, and retrieval.
• You integrate LLMs and agent systems deeply into the backend: tool use, function calling, RAG pipelines, evaluation frameworks for LLM outputs, guardrails, and production deployment.
• You translate business and customer requirements into viable technical solutions together with FDEs and Product, making pragmatic trade‑offs between speed, scalability, and maintainability.
• You prototype quickly and close to users: you put new ideas into the hands of FDEs and customers in days rather than weeks, gather early feedback autonomously, iterate closely on real workflows, and decide based on actual usage what to harden and roll out.

Requirements

• 2–5 years of professional experience as a Backend, Software, or AI Engineer in a product‑oriented environment—ideally in a product or Saa S context; startup experience is welcome.
• Solid backend foundation: Python with Fast API and Pydantic, clean API design, asynchronous programming, Postgre SQL and Redis, containerization with Docker, and experience with CI/CD pipelines.
• Strong product mindset: you don’t just ask “How do I build this?” but “What are we building this for, who is it for, and how will we measure its impact?”.

You think in terms of users, adoption, and chains of impact, not just tickets.

• Practical experience with LLMs and agent systems: prompt engineering, tool use/function calling, RAG, evaluation frameworks, and production deployment.
• Experience with cloud infrastructure, ideally AWS, and a good grasp of Kubernetes and Infrastructure‑as‑Code with Terraform—you don’t need to master everything, but you should be comfortable operating in this space.
• High level of ownership, appetite for ambiguity, and motivation to continuously develop your skills in backend engineering, agentic engineering, and product thinking.
• Clear, compelling communication with low ego, across both technical and non‑technical audiences.
• Fluent English; bonus: native German or C1‑level German.
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