Lead design and operations of GPU infrastructure and Kubernetes-based AI platform environments. Manage capacity planning, optimize training/inference workloads, define reliability and security practices, implement IaC, monitoring, and disaster recovery, and collaborate with AI engineering to evolve infrastructure.
ELEKS is looking for an Infrastructure/GPU Cluster/Platform Operations Lead in Canada.
Alberta-based candidates are strongly preferred (Calgary or Edmonton). Canada-based candidates will also be considered.
ABOUT CLIENT
Our customer is building a next-generation AI platform that enables organizations to securely develop, govern, and operationalize artificial intelligence while ensuring that sensitive data and organizational knowledge remain fully under their control. The platform combines advanced AI capabilities with enterprise-grade governance, security, and data sovereignty to support mission-critical decision-making.
The solution serves government organizations and enterprise customers operating in highly regulated and security-sensitive environments, where reliability, accountability, and trust are essential. The platform supports intelligent decision-making across strategic planning, workforce intelligence, and organizational operations, helping customers leverage AI without compromising security, compliance, or control over their data.
REQUIREMENTS
- 8+ years of Infrastructure Engineering or Platform Operations experience
- Experience managing GPU clusters for AI workloads
- Strong Kubernetes administration skills
- Experience with NVIDIA GPU technologies and CUDA ecosystem
- Experience with cloud infrastructure (Azure, AWS or GCP)
- Knowledge of storage, networking, and high-performance computing environments
- Experience implementing Infrastructure as Code (Terraform or similar)
- Strong operational leadership skills
- Experience supporting AI platform infrastructure
- Upper-Intermediate or higher level of English
RESPONSIBILITIES
- Lead GPU infrastructure design and operations
- Manage Kubernetes-based AI platform environments
- Optimize infrastructure for AI training and inference workloads
- Define operational standards and reliability practices
- Collaborate with AI engineering teams
- Implement monitoring, security, and disaster recovery strategies
- Lead infrastructure capacity planning
- Support technical roadmap and infrastructure evolution
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