DevOps Engineer, ML Infrastructure

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design, manage, and enhance large-scale, mission-critical infrastructure platforms.

• Develop and oversee cloud-native infrastructure that supports extensive ML workloads on Kubernetes.

• Implement and manage batch scheduling solutions like Volcano and Kueue to maximize GPU and compute resource efficiency.

• Create and sustain CI/CD pipelines for ML services, infrastructure, and platform components.

• Oversee and optimize Kubernetes environments, including production workloads on AWS EKS and other cloud services.

• Lead and assist in infrastructure modernization and migration projects across cloud and platform ecosystems.

• Collaborate with Data Scientists, ML Engineers, and Software Engineers to deploy machine learning solutions into production.

• Establish observability, monitoring, and alerting for distributed systems and GPU clusters.

• Ensure platform reliability, security, scalability, and operational excellence.

• Diagnose complex issues in distributed systems and identify performance bottlenecks across infrastructure and ML workloads.

• Enhance developer productivity and promote innovation through automation and AI-assisted operations.

⛳️ Requirements

• Extensive experience with Kubernetes and orchestration of large-scale workloads.

• Practical experience with Kubernetes batch schedulers such as Volcano and Kueue.

• Strong understanding of distributed systems, containerization technologies, cloud-native architectures, and microservices-based platforms.

• Experience in managing workloads on Kubernetes platforms like AWS EKS.

• Demonstrated history of delivering and supporting infrastructure migration initiatives.

• Proficient programming skills in Python or Golang.

• Familiarity with Infrastructure-as-Code and deployment tools, including Terraform and Helm.

• Experience in designing and maintaining CI/CD pipelines utilizing GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps.

• Strong skills in Linux systems administration and troubleshooting.

• Experience in building observability solutions using Prometheus and Grafana.

• Knowledge of ML lifecycle management, model deployment, and production operations.

🏝️ Benefits

• Paid time off: Vacation (12–25 days depending on grade), company-paid holidays, personal days, and sick leave.

• Medical, dental, and vision coverage, or provincial healthcare coordination in Canada.

• Retirement savings plans, including RRSP in Canada.

• Life and disability insurance.

• Employee assistance programs.

• Additional benefits provided by local policy and eligibility.

• Possible eligibility for variable incentives, bonuses, or commissions.