Back
Mid-level, tool-heavy MLOps role with broad cloud/Kubernetes requirements increases applicant competition moderately.
Core MLOps and cloud skills are transferable, but Domino-specific platform expertise raises sensitivity moderately.
Multiple mandatory technologies plus explicit 5+ years and platform expertise create strict shortlisting filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Manage and optimize Domino Data Lab platform to support the entire machine learning lifecycle.
Maintain Kubernetes clusters and Docker containers, implement CI/CD pipelines, and ensure platform stability and monitoring to minimize downtime.
Provide L2/L3 platform support and collaborate with data science teams for deployment and management of ML solutions on AWS, Azure, and GCP.
5+ years of experience in MLOps or machine learning lifecycle management.
Hands-on expertise with Domino Data Lab platform, Kubernetes, Docker, and Linux administration.
Proficiency in Python and R, and experience with CI/CD tools like Jenkins, GitLab, and GitHub.
Familiarity with cloud platforms AWS, Azure, and GCP; experience providing L2/L3 platform support.
Experienced in managing end-to-end ML operations with strong platform administration skills on Domino Data Lab.
Comfortable working with multi-cloud environments and infrastructure automation practices.
Able to troubleshoot and monitor complex ML infrastructure while collaborating cross-functionally with data science teams.