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# Senior AI Engineer

- **Company:** All Your BI
- **Location:** Rotterdam, Netherlands (Remote available)
- **Experience:** Expert
- **Salary:** €6,750.0 - €7,583.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Python (Programming Language), Machine Learning, Google Cloud, Large Language Models, Machine Learning Operations, Software Version Control, Docker
- **Published:** September 25, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a761ea738337987a

## About the Role

* Attitude and execution: energy, curiosity, and a strong bias to fix things yourself. When something breaks in production, you dig in and resolve it, you don't wait for direction. You can be trusted to own a production system without close supervision.
 * AI/ML engineering, in production: you've built and shipped LLM/GenAI systems that real people use, RAG, agents, tool-calling, not notebooks and demos. Strong Python, and you know the modern AI stack (LLM APIs across providers like Anthropic / OpenAI / open models, vector databases, an orchestration framework) well enough to know where it breaks.
 * Reliability & LLMOps: you make AI dependable, evals, observability and tracing, guardrails, prompt and version control, cost and latency budgets.
 * Client & business sense: you can tell AI that creates client value from AI that's just hype and explain which is which to a non-technical client, in strong English.
 * Nice to have: data or BI background or consultancy experience; cloud + deployment muscle (AWS / Azure / GCP, Docker, CI/CD); model distillation, structured outputs, and multimodal pipelines.

## Description

* Build and ship the AI behind our products and client solutions: LLM applications, RAG over messy real-world data, agents and tool-calling. You take these from demo-grade to production-grade.
 * Own production end to end: when something breaks, you're the one who fixes it. You implement evals, observability and tracing, and guardrails against hallucination and prompt injection, so problems surface fast and get resolved faster.
 * Keep AI affordable: track cost and latency per task, and use caching, routing, and the right model for the job rather than the biggest one.
 * Work within the architectural guidelines set by our Lead AI Engineer, and flag early when something in that direction isn't holding up in practice.
 * Translate AI capability into real client value and push back on anything that's AI for AI's sake.


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