Senior Data Engineer 100% (f/m/d) job opportunity at Julius Baer.

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DatePosted 28 Days Ago bot
Julius Baer Senior Data Engineer 100% (f/m/d)
Experience: 5-years
Pattern: full-time
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degreeBachelor's (B.A.)
loacation Madrid, Spain
loacation Madrid....Spain

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 We are looking forward to receiving your full job application through our online application tool. Further interesting job opportunities can be found on our Career site . Is this not quite what you are looking for? Set up a job alert by creating a candidate account here .

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