Location: Zürich
Work model: hybrid, 2 days on-site / 3 remote
Keywords: AI Engineer, Applied Machine Learning, LLM, RAG, MLOps

Our client, a financial services firm headquartered in Zürich, is creating a new Machine Learning Engineer role within a cross-disciplinary team. The unit develops AI applications for security, risk and back-office functions, handling sensitive data on-premise while advancing the firm's adoption of AI across departments.

As a Machine Learning Engineer, you will be at the forefront of developing advanced ML solutions, focusing on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and scalable agentic workflows.

You will:

• Build and optimise ML applications for production use
• Prototype and evaluate novel ML methods
• Scale model training on GPU clusters via Kubernetes
• Implement AIops workflows for deployment, monitoring and optimisation
• Maintain and improve internal AI infrastructure and tools

You bring:

• 3+ years of experience deploying ML systems in production
• Expert Python skills in a Linux environment
• Strong knowledge of Kubernetes, DevOps and GPU-based training
• Proven experience with LLMs, RAG pipelines and ML deployment
• Familiarity with frameworks such as PyTorch, Hugging Face or LangChain
• Exposure to UI frameworks such as Streamlit, Gradio or FastAPI
• Academic background in Machine Learning, Computer Science or a related field
• Contributions to open-source LLMs, academic research or personal ML projects are highly valued

Our client offers the opportunity to work on cutting-edge AI and ML initiatives within a highly regulated financial environment. You will gain access to large-scale GPU infrastructure, collaborate with domain experts across functions, and play a direct role in shaping the firm's AI strategy.

If you are interested, we would like to hear from your. Please apply.