The Opportunity
Join Nutanix as a DevOps Engineer in Vancouver and build the infrastructure that powers development, testing, and performance engineering for the Nutanix AI (NAI) platform. You’ll build and maintain CI/CD pipelines for software builds, testing, and releases. You’ll also deploy and operate bare-metal and cloud-based GPU infrastructure for AI inference, performance benchmarking, and product validation.
About The Team
The Nutanix AI - Infrastructure Engineering team operates the CI/CD systems, test environments, and GPU clusters that support the NAI platform. The team collaborates closely with software engineering, QA, and performance engineering teams to make environments reproducible, catch performance regressions, and improve release reliability.
In this hands-on role, you’ll work across the infrastructure stack, from server firmware, Linux, and GPU drivers to high-speed networking, storage, and Kubernetes. You’ll help keep our in-house GPU clusters reliable and provision capacity on public clouds and specialized GPU cloud platforms to validate deployments across hardware configurations.
Your Role
- Extend and maintain CI/CD pipelines for automated builds, integration testing, performance regression testing, and production releases using Jenkins, GitHub Actions, GitLab CI, or comparable tools.
- Build reproducible environments for functional, scalability, and hardware compatibility testing.
- Automate test infrastructure provisioning, software deployment, test execution, and performance metrics collection.
- Deploy and operate bare-metal GPU clusters for AI inference, benchmarking, and engineering development. Configure Linux, NVIDIA drivers, CUDA, container runtimes, networking, and storage.
- Manage BMC, BIOS, GPU, and NIC firmware updates and keep firmware versions consistent across the server fleet. Validate NVLink and NVSwitch topologies and verify GPU-to-GPU bandwidth and health.
- Tune cluster performance, GPU utilization, and workload placement across hardware configurations. Troubleshoot GPU availability, driver compatibility, PCIe and NVLink connectivity, networking, storage I/O, and distributed workload execution.
- Implement monitoring, alerting, capacity planning, and preventive maintenance to improve cluster availability.
- Provision and operate GPU infrastructure on public clouds and specialized GPU cloud platforms to support AI workloads and validate deployments.
- Deploy and operate Kubernetes clusters across bare-metal, virtualized, and cloud environments.
- Maintain Docker images, Helm charts, Kubernetes manifests, and automated deployment workflows.
- Use Ansible, Terraform, Python, and Bash to automate provisioning, configuration management, upgrades, cluster lifecycle operations, and recovery.
- Maintain version-controlled infrastructure configurations for on-premises and cloud environments, including AWS, Google Cloud, and Microsoft Azure.
- Work with software engineering, QA, and performance engineering teams to shorten feedback cycles, improve test reliability, and strengthen release quality.
What You Will Bring
- Strong proficiency in Python, Bash, or a similar scripting language, with experience building infrastructure automation and operational tools.
- 3-6 years of experience designing, maintaining, and troubleshooting CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or comparable tools.
- Strong Linux systems administration skills, including networking, storage, performance troubleshooting, and server lifecycle management.
- Hands-on experience deploying containerized applications with Docker, Kubernetes, and Helm.
- Experience deploying, maintaining, and troubleshooting development or production infrastructure in bare-metal, on-premises, or cloud environments.
- Familiarity with provisioning and operating compute infrastructure on public clouds or specialized GPU cloud platforms.
- Ability to independently investigate complex infrastructure failures, document findings clearly, and collaborate with software engineering and performance engineering teams.
Bonus Points If You Have Experience With
- Building automated performance benchmarking harnesses using Locust, GuideLLM, or Playwright.
- Deploying and operating bare-metal GPU servers or multi-node clusters with NVIDIA A100, H100, H200, B300, or similar accelerators.
- Working with the NVIDIA GPU software stack, including drivers, CUDA, NVIDIA Container Toolkit, NVIDIA GPU Operator, DCGM-based monitoring, and Kubernetes GPU scheduling.
- Operating infrastructure on specialized GPU cloud platforms such as CoreWeave, Lambda, Crusoe, or Nebius.
- Configuring and troubleshooting high-performance networking technologies such as InfiniBand, RDMA, RoCE, and high-speed Ethernet.
- Monitoring infrastructure with Prometheus, Grafana, or centralized logging systems.
- Benchmarking inference throughput, latency, and scalability for AI workloads running on vLLM, TensorRT-LLM, Triton Inference Server, or PyTorch.
- Working with distributed storage, high-performance storage, or GPU data pipelines using technologies such as NVMe, NFS, and object storage.
Learn More About the Technology: https://www.nutanixbible.com/
#NAI
Highlighted Benefits (Vancouver, Canada)
Retirement: RRSP with dollar-for-dollar matching up to 7% of base salary
Mental Health: Dedicated mental health coverage plus top-tier paramedical benefits
Family: Fully paid maternity and parental leave and generous bereavement leave, including time for the loss of a pet
Equity: RSUs and Employee Stock Purchase Plan at a 15% discount
Time Off: Company holidays, sick days, company wellness days, and vacation starting at 10 days
Work Arrangement Hybrid: This role operates in a hybrid capacity, blending the benefits of remote work with the advantages of in-person collaboration. In locations where our workplace policy applies (i.e. San Jose, Durham, Mexico City, Vancouver, Bangalore, Pune, Hoofddorp, Belgrade, Barcelona, Singapore, Sydney and Tokyo), employees are expected to work onsite a minimum of 3 days per week to foster collaboration, team alignment, and access to in-office resources. Workplace type may vary based on location and team requirements. Please speak with your recruiter for details. Additional team-specific guidance and norms will be provided by your manager.
Note: Nutanix does not assist with applications for work permits or permanent residence, including provincial nomination.
Pay Transparency - Role Location The pay range for this position at commencement of employment is expected to be between CAD $132,000 and CAD $198,000 per annual.
However, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors. Our application deadline is 40 days from the date of posting. In good faith, the posting may be removed prior to this date if the position is filled or extended in good faith.
