> Markdown version of [/jobs/ext/2731078-ai-platform-engineer](https://www.wearedevelopers.com/jobs/ext/2731078-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:** Upvest's Investment Api - **Location:** Berlin, Germany (Remote available) - **Experience:** Expert - **Salary:** €20,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Data Processing, Large Language Models, AI Platforms, Machine Learning Operations - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/ai-platform-engineer-f-m-d-upvest-co-8292064 ## About the Role * You make other people capable: You've handed something over and had it stick. You can point to work where the team you built for ended up able to carry it themselves, and you can say what made that work. * AI native: Hands-on experience shipping AI agents and end-to-end automations that ran in production and that other people depended on - not prototypes. You move fast and stay current. * Connecting LLMs to internal systems: You've securely connected models to internal tools, APIs and data sources, and you understand the access, scoping and audit questions that come with it. Experience building or operating MCP servers is a strong plus. * Automation and orchestration infrastructure: You've built and run workflow automation at production quality. We use n8n; the specific tool matters less than having done this for real. * Security-conscious collaborator: Comfortable being the technical counterpart to a Security team on AI systems - access control, data boundaries, audit trails - without owning security decisions yourself. * Regulated industry experience: You've worked in fintech or a closely adjacent field and understand compliance constraints first-hand. * Pragmatism: You define ROI, iterate and ship. A simple deterministic automation beats a complex agent when appropriate, and you can tell the difference. * Background: 5+ years in a scale-up, in roles such as platform engineer, infrastructure engineer, applied AI engineer, solutions engineer, or similar. * Location: Berlin preferred. English required; German a plus. This is not a data science role. We are looking for someone who applies and deploys AI, not someone who trains models or builds ML pipelines. ## Description You will design, build and operate the infrastructure that connects Claude and other LLMs to Upvest's internal systems - MCP servers, integrations to internal tools, APIs and data sources - and the hosted infrastructure behind the automation stack that other teams build on. You don't build in isolation. You plug into what our platform and engineering teams already run rather than standing up a parallel stack, and you're as willing to adopt and extend something that exists as you are to build it yourself. As adoption grows, you harden what you've built. Reliability, observability and evals for AI systems in production are your responsibility, not an afterthought. Make other teams self-sufficient This is half the job, not a footnote. You will work directly with people in Operations, Compliance, Growth and Finance to help them build their own workflows - sitting with them, understanding the actual process, and shipping the first version together. Then you make the second one something they can do without you: templates, patterns, guardrails, and enough teaching that the knowledge stays with the team rather than with you. The measure of success is not how many automations you've built. It's how many happen without you. If ten workflows are live and all ten depend on you personally, the platform hasn't worked. You seek out the AI champions already experimenting across the company and turn what they're doing into something secure, compliant and durable - without taking it away from them. Work alongside Security, not around them Upvest is a BaFin and FCA regulated firm. You understand the constraints that come with that and don't need convincing they're real. Security owns the controls. You are the main technical point of contact between them and everything the AI platform touches - translating how AI systems reach data and tools into terms Security can assess, and building so that access boundaries, audit trails and data handling are there by design rather than retrofitted. ## Related Videos - [AI-Augmented DevOps with Platform Engineering](https://www.wearedevelopers.com/videos/1614-ai-augmented-devops-with-platform-engineering) - [This App Reached 10,000 Users in One Week. 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