The impact you will create:

• Deploy and automate machine learning models in production using MLflow and Databricks.- Design and maintain CI/CD pipelines, incorporating Databricks unit test automation, SonarQube analysis, and notebook linting to enforce code quality and maintainability.
• Implement containerization with Docker and use Kubernetes for orchestrating scalable and resilient ML deployments.
• Manage job orchestration with Apache Airflow and Databricks Workflows to ensure reliable and automated data and model pipelines.
• Establish robust monitoring and security systems, including model performance tracking, drift detection, and load testing.
• Ensure GDPR-compliant data handling, focusing on data versioning, feature store management, and secure model lifecycle operations.
• Collaborate with cross-functional teams to share technical knowledge, enhance communication, and drive adoption of MLOps best practices.
• Utilize Google Cloud Platform (GCP) services to enhance and scale ML infrastructure.

Experience and skills you will need:

• Demonstrated ability in stakeholder communication and management
• Good understanding of related data disciplines (e.g. data engineering, analytics)
• Ability to understand, analyze and translate business requirements into data-driven solutions
• Enthusiasm for collaboration, knowledge sharing and joint learning
• Very good English skills - German is a plus, but not a must