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# AI Developer

- **Company:** BeatSquares
- **Location:** Berlin, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cursor (Graphical User Interface Elements), Python (Programming Language), Large Language Models, Model Validation, Fastapi, Front End Software Development
- **Published:** August 10, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=873da4fb50aa62f3

## About the Role

* Demonstrated product instinct and speed: you ship working things and make pragmatic trade-offs.
 * AI-native daily development (Claude, Cursor) as a real workflow, not a demo.
 * Hands-on experience with LLM orchestration (function calling, agents, evaluation).
 * You have built an eval loop, not just written prompts: baseline, iteration, measurable result.
 * You have traced LLM behaviour in production (LangSmith or equivalent) and used it to find a real bug.
 * You can explain when a deterministic pipeline is the better choice than an agent.
 * Python, and the willingness to own the deployment of your own work.
 * Fluent English

Nice-to-have:

 * Depth in FastAPI. We will also look at it if you have shown something comparable in another language.
 * Enough frontend confidence to own a feature end to end.
 * Experience with publishing or editorial workflows.
 * German.

## Description

You own LLM-powered features end to end, from the prompt to what the editor sees, and you care whether the output actually gets published. You get ideas into working shape quickly instead of waiting for the perfect spec, and you stay with them until they hold up in production.

 * Build product-facing features across the stack, with a clear bias toward shipping and iterating.
 * Work on our multi-LLM pipeline (Anthropic, OpenAI, Gemini, Fireworks/Kimi) and make deliberate calls on model selection, cost and latency.
 * Build eval loops against real editorial output: define the baseline, iterate, and be able to show what measurably improved.
 * Own traceability across our LLM systems, meaning per-step tracing and prompt provenance, so a bad output can be traced back to the call that produced it.
 * Design human-in-the-loop agent workflows: agents propose, the newsroom decides. Nothing goes live autonomously.
 * Prototype fast, harden afterwards, and ship your own work to production, including configuration, feature flags and scheduled jobs.
 * Bring your own product ideas, rather than only working through tickets.


## Related Videos

- [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi)
- [AI Driven Development](https://www.wearedevelopers.com/videos/100340-ai-driven-development)
- [Creating Industry ready solutions with LLM Models](https://www.wearedevelopers.com/videos/899-creating-industry-ready-solutions-with-llm-models)
- [Introduction to Responsible AI: Balancing Value and Risk](https://www.wearedevelopers.com/videos/1972-introduction-to-responsible-ai-balancing-value-and-risk)
- [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application)
- [Lies, Damned Lies and Large Language Models](https://www.wearedevelopers.com/videos/1231-lies-damned-lies-and-large-language-models)

## Related Articles

- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
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- [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)