VP of Presales Solutions Architecture – AI Infrastructure

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and enhance the global pre-sales organization.

• Recruit, mentor, and retain Solutions Architects and Field Engineers.

• Create the global pre-sales operating model as headcount and deal volume increase.

• Construct reusable discovery frameworks, reference architectures, TCO and benchmark models, POV playbooks, and RFP response libraries.

• Establish technical quality standards and conduct enablement on GPU architecture, high-speed fabrics, and container orchestration.

• Collaborate with Account Executives as the technical lead on strategic enterprise opportunities.

• Oversee technical sales for long-cycle opportunities lasting 6–18+ months and multi-million-dollar ACV/TCV.

• Implement structured qualification utilizing MEDDPICC or an equivalent methodology.

• Design end-to-end solutions encompassing compute, networking, storage, and orchestration.

• Provide sizing, capacity plans, and TCO comparisons against public cloud offerings and self-built solutions.

• Create and manage POVs and POCs, define success criteria, conduct performance benchmarks, and translate results into commercial success.

• Supply product, capacity, and feature requirements to product management, engineering, and supply planning teams.

• Collaborate with the NVIDIA field ecosystem on cloud partnerships, reference architectures, and joint ventures.

• Impact roadmap prioritization, packaging, and go-to-market strategy based on field insights.

⛳️ Requirements

• Leadership experience in pre-sales at a NeoCloud, hyperscaler AI organization, or accelerated-hardware vendor.

• Demonstrated success in leading technical sales within complex B2B environments.

• Preferred experience with multi-year committed-capacity or reserved-capacity deal structures.

• Proven ability to hire, develop, and lead Solutions Architecture or Field Engineering teams.

• Technical expertise to guide complex architecture and sales discussions.

• Practical experience in establishing or managing production ML workloads, including distributed training and multi-node inference.

• In-depth understanding of performance bottlenecks across interconnect, memory bandwidth, I/O, and cluster scheduling.

• Proficient in NVIDIA Hopper and Blackwell architectures, including H100/H200, GB200 NVL72, and B200.

• Familiarity with NVIDIA reference systems such as DGX, HGX, and MGX.

• Strong understanding of NVLink/NVSwitch, InfiniBand, Spectrum-X Ethernet, RDMA/RoCE, and DPUs.

• Highly preferred: Experience with Kubernetes GPU operators and device plugins, KubeVirt, Metal3/Ironic bare-metal provisioning, and Slurm.

• Knowledge of high-throughput parallel/object storage systems for AI pipelines.

• Awareness of data center physical constraints including power, cooling, and rack density.

🏝️ Benefits

• Remote work opportunities.

• Chance to engage with open source AI infrastructure, Kubernetes, GPU orchestration, and sovereign cloud technologies.

• Collaboration with the NVIDIA field ecosystem and involvement in joint ventures.