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

Tasks

• Support in developing a Python-based enterprise data hub (integrated with Oracle) and further developing the MLOps infrastructure
• Automation of CI/CD pipelines for data and ML workloads
• Accelerating model deployment
• Ensuring system stability
• Implementation of infrastructure as code and ML
• Development, training, and evaluation of machine learning models (e.g., with scikit-learn, xgboost, PyTorch) in close collaboration with data scientists
• Orchestration of end-to-end ML workflows, including preprocessing, training, hyperparameter tuning, and model validation
• Deployment and execution of models in production using containerized microservices (Docker/K8s) and REST/gRPC APIs
• Managing the MLOps lifecycle using tools such as MLflow (experiment tracking, model registry) and implementing monitoring for drift, degradation, and performance
• Refactoring exploratory code (e.g., Jupyter notebooks) into robust, testable, and versioned production pipelines
• Collaborate with data engineers to deploy and optimize the data hub to ensure reliable data flows for training and inference.
• Resolving operational issues across infrastructure, data, and model layers; participating in response and root cause analysis

Requirements

• Technical expertise: 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 capability: Ability to own the entire ML lifecycle—from training and evaluation to deployment and monitoring—with a focus on reproducibility and compliance.
• Automation and reliability: Commitment to building stable, self-healing systems with proactive monitoring and automatic recovery
• Collaboration and communication: Effective team player in agile, cross-functional environments; able to communicate clearly to technical and non-technical audiences

Education and skills

• Education: Bachelor of Science (BS) in computer science, engineering, data science, or a related field
• Certifications such as CKA, AWS/Azure DevOps Engineer, or Google Cloud Professional DevOps Engineer are advantageous.
• Technical skills: Proficient in Python, Git, and shell scripting
• Experience with CI/CD pipelines (GitLab, Jenkins), Docker, and Kubernetes

We offer