Builds and maintains the infrastructure and tooling that keeps machine learning systems reliable in production — from designing CI/CD pipelines and model deployment workflows to monitoring performance and managing model lifecycle at scale. The role works closely with ML, backend, and platform teams, contributes to automation frameworks and observability standards, and helps ensure AI models move seamlessly from experimentation to production. Requires 5+ years of MLOps or DevOps engineering experience, with a track record of operating robust ML infrastructure in production-grade environments.

Make the path from model experiment to reliable production as fast, automated, and observable as possible.

• Bachelor’s degree or higher in Computer Science, Engineering, or a related field — or equivalent practical experience.

• Design and maintain CI/CD pipelines for...