CereCore · 1 week ago

Senior ML Reliability Engineer

United States

Full-time

Remote

Senior Level

5+ years exp

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Responsibilities

Tool Development and Management: Build, manage, and maintain tools for system reliability, including dashboards, logging systems, and pager systems.

Infrastructure Maintenance: Help maintain and enhance CI/CD pipelines, logging infrastructure, and other operational systems crucial for MLOps.

Monorepo Management: Keep the monorepo up-to-date with the latest dependency and security updates, ensuring a secure and efficient development environment.

Vendor Collaboration: Assist in implementing and maintaining infrastructure and systems managed by external vendor teams.

Incident Management: Lead and participate in incident management processes, including troubleshooting, root cause analysis, and implementing corrective measures to prevent future occurrences.

Qualification

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MLOpsGoogle Cloud PlatformVertex AI

Required

Hands on technical engineering, architecture and development experience with Google Cloud Platform Vertex AI MLOps live and in production at scale.

AI/ML Knowledge: Solid understanding of AI/ML principles and technologies.

System Monitoring and Tools: Experience with system monitoring tools and observability. Knowledge of GCP, Vertex AI, or other cloud platforms is highly beneficial.

Programming and Scripting: Proficiency in programming languages such as Python and scripting for automation.

Problem-Solving Skills: Strong analytical and problem-solving skills, with the ability to work under pressure.

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.

5+ years of experience in the technology field

Proven experience in a reliability engineering role, preferably with a focus on AI/ML systems.

Experience in incident management and performance optimization.

Excellent communication and teamwork skills.