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

Why this role exists

K0rdent AI 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 customers 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 customers ML infra lead and closed large deals with cycles measured in quarters not weeks.

What youll own

Lead and build the SE / Solutions Architect team

• 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.

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 self-build.

• 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.

Qualifications :

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 (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).

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 / B200-class systems Grace-Hopper superchips) and the reference-system families (DGX HGX MGX); aware of whats 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.

Enterprise sales engineering on long high-value cycles

• Track record supporting complex B2B deals with cycles of 618 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.

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.

• Player-coach 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 / 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.

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 for AI-infra pre-sales strong candidates in this space command a premium.

• Location / travel: remote or. hub-based expect meaningful travel to customers data centers and NVIDIA/partner events.

Additional Information :

What does Mirantis offer you

• Work with an established Silicon Valley leader in the cloud infrastructure industry;
• Work with exceptionally passionate talented and engaging colleagues helping Fortune 500 and Global 2000 customers implement next-generation cloud technologies;
• Be a part of cutting-edge open-source innovation;
• Thrive in the high-energy environment of a young company where openness collaboration risk-taking and continuous growth are valued;
• Professional development and training;
• Attend conferences and working groups;
• Company outings happy hours hackathons and tech talks;
• Receive a competitive compensation package with a strong benefits plan.

We are a Leader for Container Management in G2 (#2 after AWS)!

Remote Work :

Yes

Employment Type :

Full-time