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 CHALLENGE
- Design 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 PROFILE
- Technical 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
Education and Skills Requirements - Education: Bachelor 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: 
- Proficient in Python, Git, and shell scripting
- Experienced with CI/CD pipelines (GitLab, Jenkins), Docker, and Kubernetes
- Skilled in SQL and Oracle database interactions
- Hands-on with MLOps frameworks (e.g., MLflow), model deployment, and monitoring 
- Familiarity with microservices, REST/gRPC, and basic ML model evaluation techniques

Experience:- Minimum 5 years in DevOps, SRE, or ML Engineering roles, with at least   
- 2–3 years focused on data-intensive or machine learning systems
- Experience in financial services or regulated environments is highly valued
Languages:- English is a must

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