Senior Data Engineer 100% (m/w/d)

Role Overview

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At Julius Baer, we celebrate and value the individual qualities you bring, enabling you to be impactful, to be entrepreneurial, to be empowered, and to create value beyond wealth. Let’s shape the future of wealth management together.

Support the development of a Python-based enterprise data hub (integrated with Oracle) and advance the MLOps infrastructure. This role combines DevOps excellence with hands-on machine learning engineering to deliver scalable, reliable, and auditable ML solutions. Key objectives include automating CI/CD pipelines for data and ML workloads, accelerating model deployment, ensuring system stability, enforcing infrastructure-as-code, and maintaining secure, compliant operations.

YOUR CHALLENGEDesign and maintain CI/CD pipelines for Python applications and machine learning models using GitLab CI/Jenkins, Docker, and Kubernetes 
Develop, train, and evaluate machine learning models (e.g., using scikit-learn, XGBoost, PyTorch) in close collaboration with data scientists
Orchestrate end-to-end ML workflows including pre-processing, training, hyperparameter tuning, and model validation
Deploy and serve models in production using containerised microservices (Docker/K8s) and REST/gRPC APIs
Manage the MLOps lifecycle via tools like MLflow (experiment tracking, model registry) and implement monitoring for drift, degradation, and performance
Refactor exploratory code (e.g., Jupyter notebooks) into robust, testable, and version-controlled production pipelines
Collaborate with data engineers to deploy and optimise the data hub, ensuring reliable data flows for training and inference
Troubleshoot operational issues across infrastructure, data, and model layers; participate in incident response and root cause analysis

YOUR PROFILETechnical Proficiency: Strong skills in Python, Linux, CI/CD, Docker, Kubernetes, and MLOps tools (e.g., MLflow). Practical experience with Oracle databases, SQL, and ML frameworks 
ML Engineering Aptitude: Ability to own the full ML lifecycle—from training and evaluation to deployment and monitoring—with attention to reproducibility and compliance
Automation & Reliability: Committed to building stable, self-healing systems with proactive monitoring and automated recovery
Collaboration & Communication: Effective team player in agile, cross-functional settings; able to communicate clearly across technical and non-technical audiences

• EducationBachelor of Science (BS) in Computer Science, Engineering, Data Science, or related field. Certifications such as CKA, AWS/Azure DevOps Engineer, or Google Cloud Professional DevOps Engineer are a plus
• Technical Skills