Machine Learning Engineer

About the role

Julius Baer is looking for a Machine Learning Engineer to join one of its agile development teams in its ML & AI ART. The role plays a key part in designing, building and maintaining robust, scalable AI solutions within a DevOps environment, covering the full lifecycle from concept to deployment and working closely with cross-functional teams.

Key responsibilities

• Develop, deploy and optimize machine learning and AI solutions that address complex business challenges.
• Create multi-agent systems and equip AI models with function/tool-calling capabilities.
• Design and implement RAG systems that ground AI responses in enterprise data.
• Assess and integrate AI models (e.g. LLMs) for optimal performance and reliability.
• Optimize agentic workflows and AI agents for production use cases.
• Test and optimize system prompts and few-shot examples for accurate, consistent and safe outputs.
• Evaluate ML and AI models with appropriate metrics, evaluation sets and techniques, and iterate to improve them.
• Work with platform teams, data engineers and data scientists to integrate ML solutions into existing systems.
• Take part in code reviews, testing and debugging.

Required profile

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics or a related field.
• Relevant work experience in machine learning, data science or a related field.
• Strong problem-solving, analytical and communication skills, with the ability to work with cross-functional teams.
• Ability to manage workload, prioritize tasks and meet deadlines.
• Commitment to regulatory responsibilities, including risk management and compliance with applicable laws.

Required skills

• Python with machine learning libraries and deep learning frameworks such as TensorFlow or PyTorch.
• Proven experience with Large Language Models (LLMs).
• Understanding of AI agents, agentic workflows and LLM orchestration frameworks.
• Data pre-processing, feature engineering, model selection and evaluation.
• Knowledge of probability, linear algebra and optimization.
• Version control, testing and documentation.