Your Responsibilities:
1. Set up and configure MLFlow tracking, model registry, and artifact storage for both on-prem and Azure environments
2. Integrate MLFlow with Databricks and/or on-premise infrastructure
3. Ensure secure access, data protection, and compliance with internal policies and governance standards
4. Enable MLOps automation by supporting CI/CD pipelines for ML models using tools such as Azure DevOps or GitHub Actions
5. Define and implement workflows for model training, validation, deployment, and monitoring
6. Collaborate closely with data scientists and platform engineers to operationalize machine learning models effectively
7. Produce detailed documentation of setup processes, workflows, and best practices
8. Contribute to the design of the target MLOps architecture and assist with migration planning to Azure Databricks
Your Profile:
9. Proven hands-on experience with MLFlow, both on-premise and in cloud environments
10. Good understanding of Azure Databricks and its MLFlow integration
11. Strong background in MLOps tooling, including CI/CD, model lifecycle management, and monitoring
12. Familiarity with security, governance, and compliance within ML workflows
13. Excellent communication and technical documentation skills
14. Fluent in English (written and spoken)