Senior MLOps Engineer (Ray.io)

Job Functions

• You will be a member of our AI Platform Team, supporting the next generation AI architecture for various research and engineering teams within the organization.
• You'll partner with vendors and the infrastructure engineering team for security and service availability.
• You'll fix production issues with engineering teams, researchers, data scientists, including performance and functional issues.
• Diagnose and solve customer technical problems.
• Participate in training customers and prepare reports on customer issues.
• Be responsible for customer service improvements and recommend product improvements.
• Write support documentation.
• You'll design and implement zero-downtime to monitor and accomplish a highly available service (99.999%).
• As a support engineer, find opportunities to automate as part of the problem management process, creating automation to avoid issues.
• Define engineering excellence for operational maturity.
• You'll work together with AI platform developers to provide the CI/CD model to deploy and configure the production system automatically.
• Develop and follow operational standard processes for tools and automation development, including style guides, versioning practices, source control, branching and merging patterns and advising other engineers on development standards.
• Deliver solutions that accelerate the activities phenomenal engineers would perform through automation, deep domain expertise, and knowledge sharing.

Required Skills

• Demonstrated ability in designing, building, refactoring and releasing software written in Python, C++.
• Hands-on experience with Ray.io, including workload management, cluster deployment, distributed task scheduling, and troubleshooting.
• Ability to use Ray Dashboard and CLI tools for monitoring, resource tracking, debugging distributed jobs, and resolving production issues.
• Having knowledge of Ray ecosystem libraries such as Ray Train, Ray Tune, Ray Serve, and Ray Data is a big plus.
• Experience integrating Ray with tools such as Airflow, MLflow, Dask, DeepSpeed is a big plus.
• Debugging and triaging skills.
• Cloud technologies like Kubernetes, Docker and Linux fundamentals.
• Familiar with DevOps practices and continuous testing.
• DevOps pipeline and automations: app deployment/configuration and performance monitoring.
• Test automations, Jenkins CI/CD.
• Excellent communication, presentation, and leadership skills to be able to work and collaborate with partners, customers and engineering teams.
• Well organized and able to manage multiple projects in a fast paced and demanding environment.
• Good oral/reading/writing English ability.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits