Senior Engineer — GPU Infrastructure & MLOps

Deploy and operate GPU infrastructure and the platform environments that support AI workloads. This hands-on engineering role covers cluster setup, networking, Kubernetes operations, and ongoing infrastructure lifecycle support for clients.

• Install and configure GPU server clusters, including DGX, HGX, and other vendor systems.
• Set up Infini Band and Ethernet networks for GPU workloads.
• Deploy and manage Kubernetes clusters with GPU operators, device plugins, and scheduling.
• Support client environments through the lifecycle of AI infrastructure.

• Practical experience deploying and configuring GPU servers and clusters.
• Experience with Infini Band and Ethernet networking in GPU environments.
• Kubernetes operations experience, including GPU resource integration and scheduling.
• Ability to support infrastructure used for AI workloads.

• Full-time, remote role; the listed locations are the United States or India.

For the Senior Engineer — GPU Infrastructure & MLOps position ter, we are reviewing applications now.