Head of Solutions Architecture and Sales Engineering - NeoCloud

Job Description

Head of Solutions Architecture and Sales Engineering — AI / GPU Cloud (NeoCloud) - CONFIDENTIAL

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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

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Why this role exists

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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.

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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 the

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pre-sales bar as we scale headcount and deal volume.

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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.

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What you'll own

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Lead and build the SE / Solutions Architect team

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• Hire, coach, and retain a team of sales engineers and solutions architects; define the presales operating model as the org scales.
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• Build the reusable machinery: discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo and benchmark environments, RFP response libraries.
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• Set and hold a technical quality bar across the team; run enablement so every SE can speak credibly to GPU architecture, networking, and orchestration.
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Own the technical win in large, complex deals

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• Partner with Account Executives as the technical lead on strategic and enterprise opportunities from discovery through technical close.
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• 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.
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• Architect solutions across compute, networking, storage, and orchestration; produce sizing, capacity plans, and TCO comparisons vs. hyperscalers and selfbuild.
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• Design and drive POCs/POVs: define success criteria up front, run benchmarks, and convert results into commercial momentum.
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Be the technical voice of the customer internally

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• Feed structured product and capacity requirements back to product, platform, and supply/capacity planning.
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• Work alongside the NVIDIA field and partner ecosystem (Cloud Partner program, reference architectures, joint pursuits) to strengthen deals.
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• Influence roadmap and packaging based on what you learn in the field.
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Must-have qualifications

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Real, hands-on AI/ML infrastructure experience

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• You have actually run or stood up ML workloads — distributed training and/or production inference — not just talked about them.
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• 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).
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• Comfortable in the frameworks and tooling customers use — PyTorch and the surrounding ecosystem (e.g., NCCL, CUDAlevel concepts, containers, schedulers).
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Deep knowledge of the NVIDIA platform and GPU products

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• 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.
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• Networking fluency: NVLink/NVSwitch domains, InfiniBand (Quantum) vs. SpectrumX Ethernet fabrics, RDMA/RoCE, DPUs — and why fabric choice makes or breaks large training clusters.
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• Software and platform layer: NVIDIA AI Enterprise, NIM, NeMo, Triton / TensorRTLLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystem.
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• Understands the NVIDIA Cloud Partner motion and how to cosell with NVIDIA.
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Enterprise sales engineering on long, high-value cycles

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• 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.
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• Skilled at multistakeholder navigation — ML/infra leads, platform engineering, procurement, finance, security, and executive sponsors.
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• Can build and defend a TCO/ROI model against hyperscaler and onprem alternatives, and translate performance benchmarks into commercial value.
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Proven team leadership

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• 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.
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• Playercoach mindset: still credible in the room on the hardest deals, while scaling others to do the same.
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Strongly preferred

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• Experience selling GPU cloud, HPC, or specialized infrastructure — ideally at a NeoCloud / GPUcloud provider, hyperscaler AI org, or acceleratedhardware vendor.
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• Handson with cloud-native and cluster orchestration for AI: Kubernetes (and GPU operators / device plugins), Slurm, and multicluster management approaches; familiarity with virtualized GPU / KubeVirtstyle patterns is a plus.
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• Storagefor-AI literacy — highthroughput parallel/object storage and its role in training pipelines.
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• Experience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes deals.
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• Exposure to sovereign, regulated, or government AI buyers.
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What good looks like

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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.

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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.

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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.

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• Compensation & logistics Structure: competitive base + variable tied to team bookings/attainment, plus equity.
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• Indicative OTE: senior people leader band.
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• Location / travel: expect meaningful travel to customers, data centers, and NVIDIA/partner events.
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