Senior MLOps Engineer

Responsibilities

• Primary Focus – MLOps Platform and ML Enablement
• Lead the development and improvement of the environment, platform capabilities, and operational foundations that support machine learning workflows across Oura.
• Drive the design and unification of workflows and tooling that support reliable training, orchestration, and deployment of ML systems.
• Partner with data scientists and engineers to improve the end-to-end ML lifecycle, from experimentation and training through deployment and governance, while making production ML systems easier to manage and maintain.
• Define and evolve model governance practices, including reproducibility, lineage, access controls, and operational standards.
• Support and help standardize ML tooling and workflows such as experiment tracking, model packaging, and promotion processes, with MLflow or similar tooling as a key part of the stack.
• Collaborate with teams across multiple business domains to onboard new use cases, prioritize platform improvements, and raise the maturity of shared ML capabilities.
• Identify and resolve reliability, scalability, and cost-efficiency issues in ML infrastructure running in the cloud.
• Drive standards, automation, infrastructure-as-code, CI/CD, and documentation that make ML development easier and more consistent across multiple DS teams.
• Cross-functional Growth Areas

Languages