AI Engineer / Data Scientist

AI Engineer / Data Scientist The Opportunity A lot of AI engineering roles are really just MLOps maintenance dressed up in a flashier job title. This one isn't. You'll be designing and building AI systems that sit at the heart of a financial data platform, working across the full stack from LLM architecture and RAG pipelines through to cloud infrastructure and production deployment. If you want to work on problems that are genuinely complex, in an environment that takes engineering quality seriously, this is worth a look. The Role You'll design, build, and productionise machine learning and generative AI solutions, working closely with data scientists, product owners, and business stakeholders. That means taking models from prototype to production, building the MLOps pipelines to keep them running reliably at scale, and making sure the systems you build integrate cleanly with core financial data platforms and downstream applications. You'll also be evaluating and implementing LLM-based solutions - RAG architectures, fine-tuning, prompt engineering - for both internal tooling and client-facing use cases, contributing to AI governance and responsible AI practices in a regulated environment, and mentoring junior engineers as the team grows. The role sits at the intersection of serious engineering and applied AI - and the expectation is that you're genuinely interested in both sides of that. The Company Our client is an established business operating in the financial services space, working with data and technology at enterprise scale. They're investing seriously in AI and building the engineering capability to match - which means joining now puts you close to decisions that matter, rather than inheriting a system someone else already built. What You'll Need 4+ years of professional engineering experience, with at least 2 years focused on AI/ML systems in production Strong Python skills and a track record of writing production-quality code Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn Practical experience with LLMs, including prompt engineering, RAG architectures, and/or fine-tuning Experience building and maintaining MLOps pipelines using tools such as MLflow, Kubeflow, or Vertex AI Pipelines Strong cloud platform experience on AWS, Azure, or GCP, ideally in a financial services or regulated context Proficiency with Docker, Kubernetes, and CI/CD tooling Fluency in German and English at professional level How to Apply Send your CV to the MAM Gruppe team. It doesn't need to be up to date - send what you have, or just get in touch for a conversation.