DevOps Engineer, ML Infrastructure

🕒 October 6

🇨🇦 Canada – Remote

💵 $72.1k - $138.9k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

⛑ DevOps & Site Reliability Engineer (SRE)

👻 Ghost score 2%

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Capgemini

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1967

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Capgemini is a global leader in partnering with businesses to transform and manage their operations by harnessing the power of technology. With expertise across a wide array of industries such as aerospace, automotive, banking, and healthcare, Capgemini provides a constantly evolving portfolio of services to meet the ever-changing needs of their clients. Their offerings include cloud, cybersecurity, data and artificial intelligence, and enterprise management, among others. Capgemini also emphasizes innovation and sustainability, helping companies achieve digital transformation while promoting environmental and social responsibility. Additionally, Capgemini provides career opportunities across various levels and professions, encouraging innovation and diversity in its workforce.

📋 Description

• Develop and manage cloud-native infrastructure supporting large-scale ML workloads on Kubernetes • Implement and operate batch scheduling solutions such as Volcano and Kueue to optimize GPU and compute resource utilization • Build and maintain CI/CD pipelines for ML services, infrastructure, and platform components • Manage and optimize Kubernetes environments, including production workloads running on AWS EKS and other cloud platforms • Lead and support infrastructure modernization and migration initiatives across cloud and platform ecosystems • Partner with Data Scientists, ML Engineers, and Software Engineers to productionize machine learning solutions • Implement robust observability, monitoring, and alerting for distributed systems and GPU clusters • Ensure platform reliability, security, scalability, and operational excellence • Troubleshoot complex distributed systems and performance bottlenecks across infrastructure and ML workloads

🎯 Requirements

• Strong experience with Kubernetes and large-scale workload orchestration • Hands-on experience with Kubernetes batch schedulers such as Volcano / Kueue • Solid understanding of distributed systems, containerization technologies, cloud-native architectures, and microservices-based platforms • Experience managing workloads on Kubernetes platforms such as AWS EKS • Proven track record delivering and supporting infrastructure migration projects • Strong programming skills in Python or Golang • Experience with Infrastructure-as-Code and deployment tools including Terraform / 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 using Prometheus and Grafana • Understanding of ML lifecycle management, model deployment, and production operations

🏖️ Benefits

• Paid time off based on employee grade: Vacation: 12-25 days, depending on grade • Company paid holidays • Personal Days • Sick Leave • Medical, dental, and vision coverage (or provincial healthcare coordination in Canada) • Retirement savings plans (RRSP in Canada) • Life and disability insurance • Employee assistance programs • Other benefits as provided by local policy and eligibility • Potential eligibility for variable incentives, bonuses, or commissions