> Markdown version of [/jobs/ext/85828-ai-engineer](https://www.wearedevelopers.com/jobs/ext/85828-ai-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 Engineer - **Company:** Atira - **Location:** München, Germany - **Contract:** Permanent contract - **Skills:** Computer-Aided Design, Artificial Intelligence, Information Systems, Cursor (Graphical User Interface Elements), Information Retrieval, Open Source Technology, AI Infrastructure, Large Language Models, Information Technology - **Published:** May 28, 2026 - **Apply:** https://de.indeed.com/viewjob?jk=c15eaa276413cac5 ## About the Role * Agent fluency sits on top of real engineering fundamentals. Strong skills in Python, TypeScript, or a comparable language. Paired with hands-on experience with agents, harnesses, runtimes, sandboxing, tools and evals. * You've gone further than most with AI-assisted development. Whether it's Claude Code, Cursor, Codex, or your own setup, you've invested seriously in building the skills, context, and workflows to get maximum leverage from coding agents. This isn't a nice-to-have. We think engineers who have mastered this are fundamentally more productive, and we hire accordingly * You are a strong technical decision maker that knows how to scale systems into millions of calls per day, while being able to explain the pros and cons of every solution. Yes, every solution: there are no silver bullets for complex problems. * You've demonstrated ownership and initiative: a company you started, a serious open-source contribution, a side project that actually works, a role where you owned a technical outcome others would have delegated. We want to see evidence that you build things without being told to * Degree in Computer Science, Information Systems, AI, or a related technical field from a strong technical university * Full working proficiency in English. German is not required. ## Description As an AI engineer at Atira, you will work on frontier AI technology, such as agent harnesses, runtimes, evals and self-verification in complex engineering domains. You build the frameworks and scaffolding that enable agents to reliably understand multi-stakeholder, complex industrial processes, ranging from inbound processing, product configuration, pro-active information retrieval, complex technical documentation writing and seamless systems write-backs. Misreading or wrongly interpreting a spec can cost a manufacturer six figures, so the challenges are real and the margin for error is thin. Where you trained matters less than what you've built and how honestly you can describe what broke. Atira is at a stage where you must own problems end to end, because what you ship today is what our customers use tomorrow. What you will do * Build the agent runtime and harnesses. You design and improve the core loops that let Atira's agents reason in complex engineering environments across the entire sales-engineering lifecycle. This includes the SDK, execution harnesses, and the orchestration layer that ties it together. * Build out our AI moat. You are in charge of quality & evals, customising tools & skills, improving the ontology layer that unlocks compounding process knowledge and fusing deterministic guardrails with flexible harnesses to make our agents actionable at enterprise level complexity. * Unlock industrial scale. 1000s page inbounds, excel pricing lists, technical drawings, CAD files, ERP tables, configurator rules - industrials live in multi-modal, data heavy environments where reliability is crucial. You will be responsible to continuously scale our AI systems and infrastructure: Millions of concurrent LLM requests, safely isolated sandboxes and the corresponding observability stack - no silent failures, cost blowouts, or latency explosions. * Advance our internal AI tooling. FDEs, platform engineers and the GTM team depend on tooling you build to improve Atiras internal context layer, the models we use and skills we provide them with. * Work with FDEs when customer problems hit the platform. When an FDE hits a problem that traces back to the runtime, the pipeline, or the AI infrastructure, you're the person they pull in. At a team of 12, the boundary between AI, platform and customer is thin, and you'll cross it regularly. ## Related Videos - [Microservices? Monoliths? 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