Lead the design and implementation of cutting-edge AI Data Center architectures, transforming traditional infrastructure into powerful AI Factories. This role involves crafting end-to-end AI solutions, from conceptualization and proposal development to the deployment of high-performance computing environments. You will architect robust AI networking, compute, storage, and cloud services, ensuring seamless integration and optimal performance for AI/ML workloads.

Leverage your expertise in hyperscale cloud platforms and on-premises solutions to enable advanced AI capabilities. This is an opportunity to shape the future of AI infrastructure and drive innovation in a dynamic and rapidly evolving field.

Architect and design AI-focused Data Centers, adhering to industry standards, and integrating retrofitting strategies for existing facilities. Develop comprehensive AI Data Center solutions, including proposals, RFPs, and advisory documents. Design and deploy high-performance AI networking fabrics, including InfiniBand and Spectrum Ethernet, utilizing Spine-Leaf and EVPN-VXLAN architectures. Architect AI compute solutions, focusing on NVIDIA GPU platforms (H100, H200, B200, B300, DGX), and implement GPU virtualization technologies. Design and implement advanced AI storage solutions, incorporating parallel file systems and various storage protocols. Manage AI platforms, including Kubernetes ecosystems (OpenShift, Tanzu), and orchestrate workloads using RUN and Slurm. Implement robust monitoring and observability solutions using NVIDIA tools to ensure operational excellence. Enable seamless AI service integration across hybrid and multi-cloud environments, providing guidance on adoption and best practices.

Demonstrate deep expertise in AI Data Center architecture, including power, cooling, and physical infrastructure. Possess strong knowledge of AI networking, InfiniBand, RoCE, NVIDIA Spectrum switches, EVPN-VXLAN, BGP, and network automation with Cumulus Linux. Exhibit proficiency in NVIDIA GPU platforms (DGX, H100, H200, B200, B300), GPU virtualization, and AI workload optimization. Have a solid understanding of AI storage architectures, parallel file systems, and related storage technologies. Showcase experience with Kubernetes, OpenShift, VMware Tanzu, and workload management platforms like RUN and Slurm. Familiarity with the AI software stack, including LLM Models, MLOps Platforms, and NVIDIA AI Enterprise is essential. Possess strong understanding of cloud technologies, including AWS, Azure, GCP, and OCI, with experience in designing cloud-native AI solutions.