Senior Manager, AI Cluster Deployment

Key Responsibilities

• AI and GPU Cluster Deployment & Delivery
• Oversee and partake in deployment and integration of GPU-based compute platforms from NVIDIA and other accelerator vendors
• Lead and participate in end-to-end logical deployment of large-scale AI and GPU clusters in state of the art datacenters
• Manage deployment programs spanning compute, storage, networking, power, cooling, and automation layers
• Cook up cluster architecture review for AI training, inference and distributed compute workloads
• Cook up rack-and-stack and cabling sequencing, network deployment, burn-in testing, and cluster validation
• Validate deployment readiness, topology consistency, GPU fabric performance, acceptance testing, and operational turnover processes
• Establish repeatable and documented deployment methodologies and scalable operational standards
• Networking & Fabric Management
• Lead deployment and operational validation of high-performance GPU interconnects using InfiniBand and Ethernet GPU fabric architectures
• Coordinate closely with network engineering teams on topology implementation and performance optimization
• Storage & Data Infrastructure
• Cook up with storage engineering teams on deployment and integration of high-performance storage environments supporting AI workloads
• Ensure successful implementation and operational optimization of data storage platforms
• Cook up storage throughput, latency, and GPU data delivery performance
• Automation & Provisioning
• Lead infrastructure automation initiatives for cluster provisioning and lifecycle management
• Manage deployment tooling and orchestration platforms including:
• Infrastructure-as-Code frameworks
• Automated imaging and provisioning systems (e.g. Canonical MaaS)
• Cluster monitoring and observability tools
• Drive standardization and deployment automation to improve speed, reliability, and repeatability
• Leadership & Program Management
• Build and lead high-performing technical deployment and infrastructure engineering teams
• Partner with datacenter operations, hardware vendors, networking teams, and AI platform engineering groups
• Establish strong Project Management Office (PMO) partnership while driving consistent, accurate project updates across the team and systems (e.g. Jira)
• Develop operational procedures, documentation, and deployment best practices
• Mentor engineers and technical leads across infrastructure domains

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or related field (or equivalent experience)
• 10+ years of infrastructure engineering or datacenter deployment experience
• 5+ years leading deployment or operations teams supporting large-scale AI, HPC, or GPU infrastructure
• Hands-on experience deploying and operating large GPU clusters in enterprise or hyperscale environments
• Strong expertise with:
• Canonical MaaS
• Data storage platforms
• InfiniBand and Ethernet GPU fabrics
• Linux systems administration
• GPU server architectures
• Strong understanding of:
• RDMA and RoCE networking
• High-performance storage architectures
• Cluster automation and provisioning
• Datacenter infrastructure operations
• Proven ability to manage complex cross-functional infrastructure deployment programs
• Experience deploying NVIDIA DGX SuperPOD or similar AI infrastructure solutions
• Familiarity with:
• NVIDIA networking technologies
• Spectrum-X or Quantum platforms
• AI model training infrastructure
• Liquid cooling environments
• DCIM and observability platforms
• Experience in hyperscale, cloud, or AI infrastructure environments
• Certifications in networking, Linux, Kubernetes, or cloud infrastructure are a plus

Key Competencies

• Technical leadership
• Infrastructure architecture
• Program execution
• Cross-functional collaboration
• Vendor and stakeholder management
• Problem-solving under operational pressure
• Process improvement and automation
• Excellent communication and documentation skills