Seeking a seasoned Machine Learning Operations (MLOps) expert to drive the development and operation of enterprise-scale ML platforms. This role focuses on deploying production-grade machine learning solutions within the Azure ecosystem. You will be instrumental in managing the entire ML lifecycle, from data engineering and CI/CD pipelines to deployment, sophisticated monitoring, and drift detection, operating at the critical intersection of MLOps and DevOps principles.

Spearhead the creation and management of robust, enterprise-scale ML platforms. Deploy and maintain production-grade machine learning solutions on Azure, ensuring reliability and scalability. Own the complete ML lifecycle, encompassing feature engineering, CI/CD processes, deployment strategies, ongoing monitoring, and drift analysis. Collaborate closely with platform engineering teams to integrate MLOps best practices into daily operations.

Requires 8-10 years of comprehensive experience in Data Engineering, Machine Learning, and MLOps, with a specific focus on production ML platform infrastructure for at least 4-5 years. Deep expertise in Azure Databricks, including PySpark, Spark internals, Delta Lake, and medallion architecture, is essential. Proficiency in GitHub for source control and collaborative development, alongside extensive experience with GitHub Actions or Azure DevOps YAML for CI/CD pipeline automation, is mandatory. A strong understanding of MLOps principles, such as MLflow, model lifecycle management, automated deployment, and monitoring, is critical. Experience with Azure cloud services including Azure Machine Learning, Azure Data Factory, AKS, Azure DevOps, and Azure Storage is highly preferred.