Senior Engineer — GPU Infrastructure & MLOps

CloudData.Center; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Remote role

US or India locations

Job summary

CloudData.Center is hiring for a hands-on role to deploy and operate GPU infrastructure and platform environments that support AI workloads. You will manage cluster setup, networking, Kubernetes operations, and lifecycle support for client environments.

The role focuses on GPU server clusters, InfiniBand/Ethernet networking, and Kubernetes with GPU integration to run AI workloads efficiently.

Qualifications

• Deploy and configure GPU server clusters and infrastructure.
• Experience with InfiniBand and Ethernet networking for GPU workloads.
• Strong Kubernetes operations experience including GPU resource integration and scheduling.
• Ability to support infrastructure used for AI workloads.

Responsibilities

• Install and configure GPU server clusters (e.g., DGX, HGX, vendor systems).
• Set up InfiniBand and Ethernet networks for GPU workloads.
• Deploy and manage Kubernetes clusters with GPU operators and device plugins.
• Support client environments throughout the AI infrastructure lifecycle.

Skills

GPU cluster deployment

Kubernetes orchestration

InfiniBand/Ethernet networking

AI infrastructure support

Tools

Kubernetes

DGX/HGX hardware

GPU hardware

Job description

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.