Senior Manager, Sales Engineering — AI / GPU Cloud (NeoCloud)

Mirantis is hiring a Senior Manager, Sales Engineering to build and lead its sales engineering and solutions architect function for K0rdent AI. The role owns technical wins in complex AI infrastructure deals, leads solution architecture and proof-of-concept work, and channels customer needs into product and capacity planning.

Responsibilities:

• Hire, coach, and retain a team of sales engineers and solutions architects; define the pre-sales 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
• 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 self-build
• Design and drive POCs/POVs: define success criteria up front, run benchmarks, and convert results into commercial momentum
• 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

Requirements:

• 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 (multi-node/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, CUDA-level concepts, containers, schedulers)
• Current on the NVIDIA compute stack across the Hopper and Blackwell generations (e.g., H100/H200, GB200 NVL72 / B200-class systems, Grace-Hopper superchips) and the reference-system families (DGX, HGX, MGX); aware of what's coming next-generation
• Networking fluency: NVLink/NVSwitch domains, InfiniBand (Quantum) vs. Spectrum-X 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 / TensorRT-LLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystem
• Understands the NVIDIA Cloud Partner motion and how to co-sell with NVIDIA
• Track record supporting complex B2B deals with cycles of 6–18+ months and large ACV/TCV, ideally including multi-year committed-capacity or reserved-capacity structures
• Skilled at multi-stakeholder navigation — ML/infra leads, platform engineering, procurement, finance, security, and executive sponsors
• Can build and defend a TCO/ROI model against hyperscaler and on-prem alternatives, and translate performance benchmarks into commercial value
• 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
• Player-coach mindset: still credible in the room on the hardest deals, while scaling others to do the same
• Experience selling GPU cloud, HPC, or specialized infrastructure — ideally at a NeoCloud / GPU-cloud provider, hyperscaler AI org, or accelerated-hardware vendor
• Hands-on with cloud-native and cluster orchestration for AI: Kubernetes (and GPU operators / device plugins), Slurm, and multi-cluster management approaches; familiarity with virtualized GPU / KubeVirt-style patterns is a plus
• Storage-for-AI literacy — high-throughput 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