Senior Engineer — GPU Infrastructure & MLOps

Role overview

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.

Responsibilities - Install and configure GPU server clusters, including DGX, HGX, and other vendor systems. - Set up InfiniBand 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.

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

Benefits and work setup - Full-time, remote role; the listed locations are the United States or India.