AI Data Center Architect

We are seeking a highly skilled AI Data Center Architect with hands-on experience designing and architecting modern AI infrastructure at enterprise or hyperscale scale. This is not a traditional enterprise data center architecture role. The ideal candidate will have deep expertise in GPU infrastructure, AI Factories, GPU-as-a-Service (GPUaaS), NVIDIA reference architectures, high-performance networking, AI storage, Kubernetes, and large-scale AI/LLM workloads. The successful candidate will be responsible for designing end-to-end AI infrastructure solutions, including GPU compute, high-performance networking, storage, orchestration, multi-tenancy, and AI platform services. The individual should be comfortable working directly with customers and technical stakeholders to translate AI workload requirements into scalable infrastructure architectures., * Design and architect enterprise and hyperscale AI Factory environments from the ground up.

• Develop scalable GPU infrastructure and GPU-as-a-Service (GPUaaS) architectures.
• Design infrastructure for large-scale AI training, inference, LLM, and GenAI workloads.
• Develop solutions based on NVIDIA Enterprise Reference Architectures.
• Architect NVIDIA GPU platforms including DGX, HGX, H200, GB200, and Blackwell systems.
• Design high-performance AI networking using InfiniBand, RoCE, Spectrum-X, NVLink, and BlueField DPUs.
• Perform GPU cluster sizing, capacity planning, performance optimization, and scalability analysis.
• Design high-performance storage architectures capable of supporting large-scale GPU and AI workloads.
• Architect Kubernetes-based AI platforms, including GPU scheduling, workload orchestration, resource management, and multi-tenancy.
• Design distributed training and inference infrastructure for modern LLM and GenAI workloads.
• Develop architectures for AI Cloud, GPU Cloud, and on-premises AI Factory deployments.
• Evaluate infrastructure requirements across compute, networking, storage, orchestration, and AI platform layers.
• Work with engineering, infrastructure, cloud, networking, and AI/ML teams to develop end-to-end solutions.
• Participate in customer-facing architecture discussions, technical workshops, solution design, and presentations.
• Create high-level and low-level architecture designs, technical documentation, reference architectures, and solution proposals.
• Evaluate emerging NVIDIA technologies and AI infrastructure trends and incorporate them into future architectures.

• 4+ years of experience designing AI-focused infrastructure, accelerated computing platforms, or GPU-based data center environments.
• Proven experience architecting AI Factories, GPU clusters, or GPUaaS platforms.
• Experience designing environments supporting 100+ GPUs is strongly preferred.
• Deep understanding of NVIDIA AI infrastructure and Enterprise Reference Architectures.

Hands-on architecture experience with one or more of:

• NVIDIA DGX
• NVIDIA HGX
• H200
• GB200
• NVIDIA Blackwell platforms

Strong understanding of:

• InfiniBand
• Spectrum-X
• RoCE
• NVLink / NVSwitch
• BlueField DPUs
• 400G/800G networking
• Experience with GPU cluster sizing, performance optimization, scaling, and capacity planning.
• Strong understanding of LLM training and inference infrastructure.
• Experience with Kubernetes for AI/GPU workloads.
• Knowledge of GPU scheduling, workload orchestration, and multi-tenant GPU environments.
• Experience designing distributed AI/ML training infrastructure.
• Strong knowledge of high-performance storage architectures for AI workloads.
• Understanding of MLOps, AI platforms, and GenAI infrastructure.
• Ability to design complete AI infrastructure solutions spanning compute, networking, storage, orchestration, and platform services., * Experience designing on-premises AI Factories, Sovereign AI infrastructure, or GPU Cloud platforms.
• Experience with Azure, AWS, or Google Cloud AI infrastructure.
• Experience with NVIDIA AI Enterprise and the broader NVIDIA AI software ecosystem.
• Experience with large-scale distributed training frameworks and AI workload orchestration.
• Experience with AI infrastructure benchmarking and performance optimization.
• Customer-facing architecture, consulting, solution engineering, or technical pre-sales experience.
• Experience developing technical proposals, architecture diagrams, bills of materials, and solution designs.