Head of Solutions Architecture and Sales Engineering - NeoCloud

Reports to: CBO Team: People-leader for a team of Sales Engineers / Solutions Architects (player-coach at hire, scaling to full leader)

Location: [Remote / Hybrid — primary hubs TBD] · Some travel to customer sites, data centers, and NVIDIA GTC / partner events Why this role exists Our product is the orchestration layer that turns raw, disaggregated GPU infrastructure into a multi-tenant, production-ready AI cloud — without locking companies into a single hyperscaler or hardware vendor. We sell accelerated compute: GPU clusters, bare metal, and managed AI infrastructure to Neoclouds, AI-native startups, enterprise AI teams, research labs, and sovereign/regulated buyers. These are technical, high-value, long-cycle deals where the sale is won or lost on credibility: whether we can architect the right cluster, model the real TCO, prove performance, and de-risk a customer's move onto our platform. This person owns the technical win. They build and lead the sales engineering function that turns "interested" into signed, multi-year committed-capacity contracts, and they set thepre-sales bar as we scale headcount and deal volume. This is not a demo-jockey role. We need someone who has genuinely stood up training and inference workloads, argued interconnect topology with a customer's ML infra lead, and closed large deals with cycles measured in quarters, not weeks. What you'll own Lead and build the SE / Solutions Architect team Hire, coach, and retain a team of sales engineers and solutions architects; define the presales operating model as the org scales. Build the reusable machinery: discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo and benchmark environments, RFP response libraries. Set and hold a technical quality bar across the team; run enablement so every SE can speak credibly to GPU architecture, networking, and orchestration. Own the technical win in large, complex deals Partner with Account Executives as the technical lead on strategic and enterprise opportunities from discovery through technical close. Run qualification with a real methodology (MEDDPICC or equivalent) — surface the economic buyer, decision criteria, and the technical champion, and build the win plan around them. Architect solutions across compute, networking, storage, and orchestration; produce sizing, capacity plans, and TCO comparisons vs. hyperscalers and selfbuild. Design and drive POCs/POVs: define success criteria up front, run benchmarks, and convert results into commercial momentum. Be the technical voice of the customer internally Feed structured product and capacity requirements back to product, platform, and supply/capacity planning. Work alongside the NVIDIA field and partner ecosystem (Cloud Partner program, reference architectures, joint pursuits) to strengthen deals. Influence roadmap and packaging based on what you learn in the field. Must-have qualifications Real, hands-on AI/ML infrastructure experience You have actually run or stood up ML workloads — distributed training and/or production inference — not just talked about them. Practical fluency in the training and inference lifecycle: data pipelines, distributed training (multinode/multi-GPU), fine-tuning, and serving; you understand where bottlenecks actually live (interconnect, memory bandwidth, I/O, scheduling).Comfortable in the frameworks and tooling customers use — PyTorch and the surrounding ecosystem (e.g., NCCL, CUDAlevel concepts, containers, schedulers).Deep knowledge of the NVIDIA platform and GPU products Current on the NVIDIA compute stack across the Hopper and Blackwell generations (e.g., H100/H200, GB200 NVL72 / B200class systems, GraceHopper superchips) and the referencesystem families (DGX, HGX, MGX); aware of what's coming next-generation. Networking fluency: NVLink/NVSwitch domains, InfiniBand (Quantum) vs. SpectrumX Ethernet fabrics, RDMA/RoCE, DPUs — and why fabric choice makes or breaks large training clusters. Software and platform layer: NVIDIA AI Enterprise, NIM, NeMo, Triton / TensorRTLLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystem. Understands the NVIDIA Cloud Partner motion and how to cosell with NVIDIA.Enterprise sales engineering on long, high-value cycles Track record supporting complex B2B deals with cycles of 6–18+ months and large ACV/TCV, ideally including multiyear committed-capacity or reserved-capacity structures. Skilled at multistakeholder navigation — ML/infra leads, platform engineering, procurement, finance, security, and executive sponsors. Can build and defend a TCO/ROI model against hyperscaler and onprem alternatives, and translate performance benchmarks into commercial value. Proven team leadership Has hired, developed, and led a sales engineering / solutions architecture team (or clearly demonstrated the readiness to), including building process and enablement from a light or greenfield starting point. Playercoach mindset: still credible in the room on the hardest deals, while scaling others to do the same. Strongly preferred Experience selling GPU cloud, HPC, or specialized infrastructure — ideally at a NeoCloud / GPUcloud provider, hyperscaler AI org, or acceleratedhardware vendor. Handson with cloud-native and cluster orchestration for AI: Kubernetes (and GPU operators / device plugins), Slurm, and multicluster management approaches; familiarity with virtualized GPU / Kube Virtstyle patterns is a plus. Storagefor-AI literacy — highthroughput parallel/object storage and its role in training pipelines. Experience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes deals. Exposure to sovereign, regulated, or government AI buyers. What good looks like First 90 days: deep on our platform and differentiators; embedded as technical lead on the top active opportunities; a clear read on the current team, gaps, and the pre-sales process to fix first.6 months: a repeatable POV and TCO framework in use across the team; measurable improvement in technical-win rate and POC-to-close conversion; a hiring plan (or hires) closing the biggest coverage gaps.12 months: a scaled, high-credibility SE org that AEs actively pull into strategic deals; SE involvement correlated with larger deal size, faster technical close, and higher win rate on the deals that matter most. Compensation & logistics Structure: competitive base + variable tied to team bookings/attainment, plus equity. Indicative OTE: senior people leader band. Location / travel: expect meaningful travel to customers, data centers, and NVIDIA/partner events.