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Specialized MLOps skillset but mid-level seniority and known services brand increase applicant density.
MLOps infrastructure skills transfer across industries, but Domino platform knowledge adds moderate domain specificity.
Requires 5+ years plus mandatory Domino, Kubernetes, CI/CD, multi-cloud and platform support skills.
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Manage and optimize Domino Data Lab platform across entire machine learning lifecycle including deployment and monitoring.
Maintain Kubernetes clusters and Docker containers to support scalable workloads and ensure platform reliability.
Provide L2/L3 platform support, monitoring, troubleshooting, and collaborate with data science teams to streamline workflows across AWS, Azure, and GCP environments.
5+ years experience in MLOps or machine learning lifecycle management.
Expertise in Domino Data Lab platform, Kubernetes, Docker, Python, R, and CI/CD tools like Jenkins, GitLab, GitHub.
Proficiency in Linux administration with ability to monitor and troubleshoot platform issues.
Experience with cloud platforms AWS, Azure, GCP and platform support at L2/L3 levels.
Senior engineer capable of leading MLOps initiatives including infrastructure automation and platform reliability.
Experienced with multi-cloud deployment and managing containerized environments (Kubernetes, Docker).
Strong collaboration skills with data science teams to enhance machine learning workflow efficiency and integration.