Jobs / Capgemini Engineering
DevOps Engineer (ML Infrastructure)
Capgemini Engineering · Toronto, ON, Canada
Toronto, ON, Canada72,056-138,940 CAD/yearlyRemote
Remuneration
72,056-138,940 CAD/yearly
Location
Toronto, ON, Canada
Visa sponsorship
Not specified
Job summary
Build, operate, and evolve large-scale infrastructure platforms for high-volume machine-learning workloads, emphasizing reliability, scalability, automation, and operational excellence. Develop cloud-native Kubernetes infrastructure, manage batch scheduling with Volcano or Kueue, maintain CI/CD pipelines, and improve monitoring across AWS EKS and distributed systems. Partner with data scientists, ML engineers, and software engineers to productionize machine-learning solutions.
Benefits
Paid time offVacation of 12–25 days, depending on employee gradeCompany-paid holidaysPersonal daysSick leaveMedical, dental, and vision coverage or provincial healthcare coordination in CaRetirement savings plan, including RRSP in CanadaLife and disability insuranceEmployee assistance programs
Qualifications
- Strong experience with Kubernetes and large-scale workload orchestration.
- Hands-on experience with Kubernetes batch schedulers such as Volcano or Kueue.
- Understanding of distributed systems, containerization, cloud-native architectures, and microservices platforms.
- Experience managing workloads on Kubernetes platforms such as AWS EKS.
- Experience delivering and supporting infrastructure migration projects.
- Programming skills in Python or Golang.
- Experience with infrastructure-as-code and deployment tools such as Terraform and Helm.
- Experience designing and maintaining CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps.
- Strong Linux systems administration and troubleshooting skills.
- Experience building observability solutions with Prometheus and Grafana.
- Understanding of machine-learning lifecycle management, model deployment, and production operations.
Responsibilities
- Develop and manage cloud-native infrastructure for large-scale machine-learning workloads on Kubernetes.
- Operate Volcano or Kueue batch schedulers to optimize GPU and compute resource utilization.
- Build and maintain CI/CD pipelines for machine-learning services, infrastructure, and platform components.
- Manage and optimize Kubernetes environments, including production workloads on AWS EKS.
- Lead and support infrastructure modernization and migration initiatives.
- Partner with data scientists, ML engineers, and software engineers to productionize machine-learning solutions.
- Implement observability, monitoring, and alerting for distributed systems and GPU clusters.
- Ensure platform reliability, security, scalability, and operational excellence.
- Troubleshoot distributed systems and performance bottlenecks across infrastructure and machine-learning workloads.
Skills
AWSAzureAzure DevOpsEKSGitHubGitHub ActionsGitLabGitLab CIGoGrafanaHelmJenkinsKubernetesLinuxPrometheusPythonTerraform
Languages
PythonGolang
Industry
Machine-learning infrastructureEngineering services
Relocation
No