Cloud Platform Engineer - ML DevOps

- Design, construct, and uphold infrastructure for machine learning experiments, model training, and deployment - Develop and manage continuous integration and continuous delivery (CI/CD) pipelines for machine learning workflows including data ingestion, model training, testing, and deployment - Implement and oversee machine learning platforms (e.g., Azure MLStudio, Fabric, MLflow, Kubeflow, SageMaker, Vertex AI) for reproducibility and scalability - Create tools and environments that facilitate data versioning, model tracking, and artifact management - Collaborate with data scientists to provide self-service access to computing resources and production systems - Monitor, log, and generate alerts regarding the health of machine learning systems and model performance in production - Advocate for best practices in MLOps across teams, including governance, model validation, and rollback strategies - Ensure infrastructure security, cost-effectiveness, and regulatory compliance - Engage in daily paired programming and test-driven development while writing software and creating products - Partake in executing the strategy with a focus on customer needs and desires - Establish continuous integration, continuous delivery, and continuous deployment pipelines and practices - Engage in retrospectives to collect feedback and formulate actionable items for team and product improvement - Participate in iteration planning meetings to ensure a shared understanding of each story and tasks in the teams backlog