AI Associate - m/f/d

Help Us Change the Way the World Works Build something that matters. Langdock exists to change the way the world works, bridging the gap between what technology can do and what people actually do with it. We bring all leading AI models into one secure, model-agnostic platform and make them usable across entire organizations. Over 10,000 companies use our platform every day, from fast-growing startups to some of Europe's largest enterprises. Their employees open Langdock to draft strategies, analyze documents, or automate workflows - helping them to work smarter, think more creatively, and reach their full potential. About the Role This is a full-time, 12-month program at our Berlin HQ for people who want to understand AI rather than just use it. It begins where our product meets the real world: customer support. Many support tickets are a small puzzle about how large language models and cloud software behave in production. Why did the same prompt give two different answers? Why did a long document get cut off? Why is one model fast today and slow tomorrow? Why did an agent call the wrong tool? Why did an API integration time out? You will be the person who works out what is actually going on and explains it clearly. For most of your first year, the support queue is your home base and your classroom. Few jobs expose you to as many real AI systems, real enterprise environments, and real failure modes, every single day. The purpose of the year: by month twelve, you should understand our platform and operations deeply, from the chat interface down to the models and infrastructure behind it. A Langdock business team should want to hire you permanently, and you should have found the work that energizes you most. What You Will Learn to Understand Customers ask the questions that the rest of the world is still figuring out. To answer them, you will build real intuition for: How LLMs actually behave : tokens and context windows, sampling and non-determinism, hallucinations, prompt sensitivity, system prompts, and why models from different providers react differently to the same input. Agents, tools, and retrieval : how agents plan and call tools, how knowledge bases and document search feed context to a model, and where these chains tend to break. Cloud software in production : APIs, authentication and SSO, rate limits, latency, integrations, multi-tenant enterprise setups, data residency, and access control. Inference and compute : what happens when a request hits a model, why throughput, latency, and cost trade off against each other, and how running our own inference changes what we can offer customers. Daily operational tasks : resolving billing issues, reporting bugs and being the point of for our customers. You do not need to know all of this on day one. You need to be the kind of person who cannot stop asking why until you do. How the Year Works Onboarding: Learn the platform by supporting it. You go through an intensive onboarding into our product