# Senior AI Evals Engineer (AI Quality & Safety) - Libra-Legal AI Assistant (m/w/d) at Wolters Kluwer

Wolters Kluwer is hiring a Senior AI Evals Engineer (AI Quality & Safety) - Libra-Legal AI Assistant (m/w/d) in Berlin, Germany. Level rates it Works on AI ●●●○; you can [apply on Wolters Kluwer](https://jobsbylevel.com/go/3ebc6274-a093-4a5e-a102-354908934bc6?utm_medium=ai-agent).

AI Level 3, AI centrality 63 out of 100. DEU - Berlin.

## Details

- Company: [Wolters Kluwer](https://jobsbylevel.com/companies/wolters-kluwer)
- AI level: AI Level 3 (score 63 out of 100)
- Location: DEU - Berlin
- Posted: November 5, 2026
- Apply: https://jobsbylevel.com/go/3ebc6274-a093-4a5e-a102-354908934bc6?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-ai-evals-engineer-ai-quality-safety-libra-legal-ai-assistant-m-w-d-at-wolters-kluwer-00ddd5

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## Description

About Us There are hundreds of thousands of lawyers across Europe, and at Libra we're transforming how they work. Our AI platform combines deep legal reasoning with cutting-edge generative technology, fundamentally changing how lawyers research, draft, and deliver legal work. We operate with the speed and ownership of a startup, while being backed by the scale and stability of an established leader. Libra is an independent business unit within the Legal & Regulatory division of Wolters Kluwer, a leading global provider of information, software, and services for professionals. For 180 years, Wolters Kluwer has supported and simplified the work of experts and organizations through innovative solutions, relying today on more than 20,000 colleagues worldwide to bring that vision to life. About The Role As an Evals Engineer (m/w/d) at Libra, you'll be the first dedicated hire on a new team that owns how we measure quality: the datasets, rubrics, judges and harnesses that decide whether an AI feature is good enough to ship, and the automated optimization that runs against them. Optimizing a system against a target is newly automatable: LLM optimizers like GEPA now propose and test the candidates themselves. Nobody has to invent the experiments any more, and the system will improve in whatever direction the evals point, whether or not that's where you meant to go. That makes defining what good means the highest-leverage work we do, and it has to be done task by task, from scratch, for a legal AI platform at European scale. You'll work in a lean, agile environment with AI Engineers, Legal Engineers and Product, based at the vibrant Merantix AI Campus in Berlin, surrounded by a community of AI innovators. What you'll do Build and own the eval platform: datasets, judges, harnesses, regression gates, cost and quality in one view, on Python , FastAPI and Langfuse . Make it self-service, so AI Engineers can evaluate and tune their own features without going through you, and eval-driven development becomes the most effective path rather than a tax. Map the quality landscape: good means something different for research, drafting, summarisation and retrieval, and again per jurisdiction. Work out what a defensible measure looks like for each, going first on the ones nobody has evaluated before and then making them repeatable without you. Design the rubrics and set the standard for LLM-as-judge: turn Legal Engineers' and subject-matter experts' judgment into version-controlled criteria, keep judges recalibrated as models and jurisdictions change, and make authoring cheap enough to do at volume on privileged material. Deep-dive results and traces until you can say why something failed, then find the lever that moves it and automate the fix. Unhobble the optimizer: instrument the app so prompts, hyperparameters and harness architecture become levers a search can safely pull, then run automated optimization (GEPA, DSPy) over them against a fitness function you trust, widening that surface as you go. Make cheaper models win: treat quality per euro as a first-class metric, instrumented per call, and find the configuration where a smaller model matches or beats an expensive one. Own guardrails: ungrounded advice, invented or misattributed citations, jurisdiction and language leakage, prompt injection from ingested documents. Design them, red-team them, prove they hold. What you'll bring Education Bachelor's degree or equivalent in a relevant technical field (e.g. Computer Science, Software Engineering, Statistics, Data Science); advanced degree is a plus. Experience Minimum 5 years in software engineering, at least 1 building LLM-powered products in production . Strong Python : FastAPI , modern tooling, and the data stack (pandas, numpy, notebooks), because much of this job is analysis. Hands-on experience designing evaluations : datasets, rubrics, LLM-as-judge, benchmarking, human labelling. Solid security and data-privacy practice. You'll handle

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