Job Title: AI Data Center Architect
Location: Plano, TX
Job Summary
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.
Key Responsibilities
- 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.