VP of Presales Solutions Architecture – AI Infrastructure
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Job Description
Leading Mirantis’s pre-sales architecture organization for k0rdent AI, a Kubernetes-native GPU infrastructure platform. Driving strategic enterprise technical wins, scalable field enablement, and AI infrastructure roadmap influence.
Responsibilities:
- Build and scale the global pre-sales organization
- Hire, coach, and retain Solutions Architects and Field Engineers
- Establish the global pre-sales operating model as headcount and deal volume scale
- Build reusable discovery frameworks, reference architectures, TCO and benchmark models, POV playbooks, and RFP response libraries
- Set technical quality standards and run enablement on GPU architecture, high-speed fabrics, and container orchestration
- Partner with Account Executives as the technical executive on strategic enterprise opportunities
- Lead technical sales for long-cycle opportunities of 6–18+ months and multi-million-dollar ACV/TCV
- Execute structured qualification using MEDDPICC or equivalent
- Architect end-to-end solutions across compute, networking, storage, and orchestration
- Deliver sizing, capacity plans, and TCO comparisons against public clouds and self-built alternatives
- Design and drive POVs and POCs, define success criteria, run performance benchmarks, and convert results into commercial momentum
- Feed product, capacity, and feature requirements to product management, engineering, and supply planning teams
- Partner with the NVIDIA field ecosystem on cloud partnerships, reference architectures, and joint pursuits
- Influence roadmap prioritization, packaging, and go-to-market strategy based on field feedback
Requirements:
- Pre-sales leadership experience at a NeoCloud, hyperscaler AI organization, or accelerated-hardware vendor
- Proven track record leading technical sales in complex B2B environments
- Experience with multi-year committed-capacity or reserved-capacity deal structures preferred
- Track record of hiring, developing, and leading Solutions Architecture or Field Engineering teams
- Technical depth to lead complex architecture and sales discussions
- Hands-on experience standing up or operating production ML workloads, including distributed training and multi-node inference
- Deep understanding of performance bottlenecks across interconnect, memory bandwidth, I/O, and cluster scheduling
- Fluency across NVIDIA Hopper and Blackwell architectures, including H100/H200, GB200 NVL72, and B200
- Experience with NVIDIA reference systems including DGX, HGX, and MGX
- Deep understanding of NVLink/NVSwitch, InfiniBand, Spectrum-X Ethernet, RDMA/RoCE, and DPUs
- Strongly preferred: Kubernetes GPU operators and device plugins, KubeVirt, Metal3/Ironic bare-metal provisioning, and Slurm
- Familiarity with high-throughput parallel/object storage systems for AI pipelines
- Familiarity with data center physical constraints including power, cooling, and rack density
Benefits:
- Remote work
- Opportunity to work with open source AI infrastructure, Kubernetes, GPU orchestration, and sovereign cloud technologies
- Collaboration with NVIDIA field ecosystem and participation in joint pursuits
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