Head of Solutions Architecture and Sales Engineering - NeoCloud
1 day ago
Syracuse
Head of Solutions Architecture and Sales Engineering — AI / GPU Cloud (NeoCloud) - CONFIDENTIAL 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 the pre-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 / KubeVirtstyle 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.