SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Oura is seeking a Senior MLOps Engineer to join the Data Engineering & Analytics team. The role focuses on designing and evolving the platforms, workflows, and governance practices that enable machine learning teams to develop, train, deploy, and operate ML systems reliably at scale.
Key responsibilities include leading the development of MLOps platform capabilities and operational foundations supporting ML workflows across the organization. You will drive the design and unification of workflows and tooling for reliable training, orchestration, and deployment of ML systems. Working closely with data scientists and engineers, you'll improve the end-to-end ML lifecycle from experimentation through deployment and governance, making production ML systems easier to manage and maintain.
You will define and evolve model governance practices including reproducibility, lineage, access controls, and operational standards. The role involves standardizing ML tooling and workflows such as experiment tracking, model packaging, and promotion processes using tools like MLflow. You'll collaborate across multiple business domains to onboard new use cases, prioritize platform improvements, and raise the maturity of shared ML capabilities.
Additional responsibilities include identifying and resolving reliability, scalability, and cost-efficiency issues in ML infrastructure running in the cloud, driving standards and automation through infrastructure-as-code and CI/CD practices, and improving observability for ML workflows. You'll partner with data engineering and platform teams to align ML systems with broader data platform governance practices and help shape best practices for building, shipping, and maintaining production ML systems.
Required qualifications include 5+ years of experience in MLOps, machine learning engineering, platform engineering, data engineering, or related fields. You need hands-on experience running production workloads in AWS with strong cloud infrastructure understanding. Strong knowledge of the ML lifecycle, familiarity with tools like MLflow, workflow orchestration, infrastructure-as-code, and CI/CD practices are essential. Experience with secure access patterns, governance controls, and shared cloud/data platform services is required. You should demonstrate proven ability to drive standards and improvements across multiple teams, strong communication and collaboration skills, and comfort operating in a distributed team with high autonomy.
