Senior Kubernetes Engineer - AI GPU Cluster Infrastructure

Job Description:

• 5+ years of experience with Kubernetes in production environments
• Experience managing Kubernetes clusters for GPU workloads
• Strong knowledge of Kubernetes networking and cluster architecture
• Experience configuring RDMA and RoCEv2 networking for Kubernetes clusters
• Hands-on experience with Kubernetes CLI tools and cluster troubleshooting
• Optional:
• Experience with NVIDIA NCLL and/or GPU ecosystem tools
• Experience with AI infrastructure environments and automation

Job Responsibilities:

• Deploy, configure, and manage Kubernetes clusters for AI workloads
• Ensure proper configuration of RoCEv2 and RDMA networking within Kubernetes clusters
• Troubleshoot node failures and ensure cluster stability and recovery
• Support performance testing and optimization of AI workloads running on Kubernetes
• Work closely with infrastructure, compute, and networking teams
• Implement best practices for GPU cluster management and reliability

Must have:

• Kubernetes
• Linux administration
• RDMA/ROCEv2
• Troubleshooting
• Nice to have:
• NCCL
• Automation/deployment automation

Education: Bachelor''s or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field. 

GlobalLogic estimates the starting pay range for this role to be performed in Remote to be $135,000 to $140,000 and reflects base salary only. This pay range is provided as a good-faith estimate, and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.