> Markdown version of [/jobs/ext/4064774-platform-engineer](https://www.wearedevelopers.com/jobs/ext/4064774-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). --- # Platform Engineer - **Company:** Postaladdress - **Location:** München, Germany - **Experience:** Expert - **Salary:** €60,000.0 - €90,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, PostgreSQL, Caching, Low Latency, Real Time Data, Front End Software Development, Docker - **Published:** October 5, 2026 - **Apply:** https://startup.jobs/senior-platform-engineer-w-m-d-clera-10285562 ## About the Role * At least 3 years of production backend engineering experience, including 3 or more years working with TypeScript. * Experience owning production platform foundations and improving reliability and performance under real-world load. * Production experience with Docker and Kubernetes. * Strong system design skills, including the ability to reason about performance, latency, and cost trade-offs. * Solid knowledge of PostgreSQL, APIs, queues, and caching. * Experience with instrumentation, observability, and root-cause analysis; familiarity with streaming responses or language model request routing is valuable. * Comfort contributing to frontend code when needed. ## Description Join the platform engineering team at an AI product studio and help build the foundation for reliable, high-performing AI products. You will shape how data, streaming responses, and language model requests move through production systems, helping the platform stay dependable as usage grows., * Integrate AI products with external data sources and multiple systems, handling rich context and real-time data flows. * Route streaming responses and language model requests across providers, using fallback and cost logic to maintain correctness under production load. * Monitor latency distributions, costs, and error rates, then act proactively to prevent system degradation. * Make architectural decisions that improve reliability and performance while keeping systems simple and maintainable. * Build and improve instrumentation and observability to identify missing signals and investigate complex production issues.