> Markdown version of [/jobs/ext/3681934-ai-platform-engineer](https://www.wearedevelopers.com/jobs/ext/3681934-ai-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineer - **Company:** Raisin GmbH - **Location:** Berlin, Germany - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Systems Engineering, Audit Trail, Code Review, Continuous Integration, Programming Tools, Java Virtual Machine (JVM), Python (Programming Language), Machine Learning, OAuth, OpenID, Role-Based Access Control, Single Sign-On, Software Engineering, TypeScript, Large Language Models, AI Coding Agents, Backend, Agentic-AI, Rate Limiting, Build Management, AI Platforms, Api Gateway, Model Context Protocol - **Published:** October 11, 2026 - **Apply:** https://startup.jobs/ai-platform-engineer-m-f-d-raisin-8633965 ## About the Role * 5+ years as a software engineer in backend, platform, or infrastructure roles, including production systems that other teams depend on. * Experience building for internal customers: a platform, developer tooling, or shared services used by other engineers. * Strong skills in at least one programming language (Python preferred; TypeScript and JVM languages also relevant). * Solid cloud and delivery fundamentals: AWS or another major cloud, containers, infrastructure as code, and CI/CD. * Practical experience with LLM APIs or AI tooling beyond experiments, and you use AI coding assistants in your daily work. * You are comfortable with evolving scope, make pragmatic trade-offs, and explain them clearly to technical and non-technical colleagues. Nice to have * Experience with LLM gateways, the Model Context Protocol (MCP), managed model services, or agent runtimes. * You have rolled out AI-assisted development workflows for other engineers, such as agent instructions, reusable skills, or automated code review. * Identity and access: OAuth 2.0 / OIDC, SSO, role-based access control, or delegated access. * API gateways, rate limiting, multi-tenant isolation, or observability (OpenTelemetry or similar). * A feel for the cost, latency, and quality trade-offs of different models. You don't need to tick every box. If you have strong platform or developer-tooling experience and real curiosity about AI, we would like to hear from you. ## Description As an AI Platform Engineer, you build and run the platform that lets teams use AI safely, and you help engineering teams bring AI into how they build software. You own your work from the first design to production and work directly with the people who use it. You won't do all of the following at once: you will go deep in a few areas and contribute to the rest. Build and run the platform * Design, build, and operate shared services that give teams governed access to AI models and tools, including authentication, routing across providers, usage limits, guardrails, and audit logging. * Build safe ways for AI agents to connect to internal systems and data, with clear identity, permissions, and review. * Run what you build in production: infrastructure as code, observability for reliability, usage, and cost, and incident response. Bring AI into how we build software * Design and build agentic workflows that support engineers across the development lifecycle, from planning and implementation to code review, testing, and release. * Create reusable building blocks for AI tools, such as agent instructions, skills, and integrations with engineering systems, that teams can adopt and extend. * Help engineering teams adopt AI-assisted development in a way that improves quality as well as speed, and measure the effect on delivery. Treat the platform as a product for internal customers * Work with engineers, product managers, and business teams to understand their needs and turn them into platform capabilities. * Make the platform and its tools easy to adopt through self-service, sensible defaults, and clear documentation. * Measure adoption, reliability, cost, and impact, and use that data to decide what to build next. Own technical decisions * Lead the design of new capabilities from first idea to production, including build-versus-buy choices, and document decisions so others can follow the reasoning. * Work with security, legal, and compliance colleagues to turn their requirements into controls the platform enforces automatically. * Make principled model choices based on cost, latency, and quality. * Raise the bar for the engineers around you through design reviews, pairing, and sharing what you learn.