Head of Solutions Architecture – AI Infrastructure

This is a fully remote position, open to applicants in Kazakhstan, +1 more country.

📋 Description

• Develop and expand the global pre-sales organization and its operational framework.

• Recruit, mentor, and retain Solutions Architects and Field Engineers.

• Create reusable assets for the field, such as discovery frameworks, reference architectures, TCO and benchmark models, POV playbooks, and RFP response libraries.

• Set technical quality benchmarks and train teams on GPU architecture, high-speed fabrics, and container orchestration.

• Collaborate with Account Executives as the technical lead on strategic enterprise opportunities with sales cycles ranging from 6 to 18+ months and involving multi-million-dollar ACV/TCV.

• Implement structured qualification processes utilizing MEDDPICC or similar methodologies.

• Design end-to-end solutions encompassing compute, networking, storage, and orchestration.

• Provide sizing, capacity planning, and TCO comparisons against public cloud offerings and self-built alternatives.

• Design and manage POVs/POCs, establish success criteria, conduct performance benchmarks, and translate results into commercial opportunities.

• Relay product, capacity, and feature requirements to product management, engineering, and supply chain teams.

• Collaborate with the NVIDIA field ecosystem on reference architectures and joint initiatives.

• Impact roadmap prioritization, packaging, and go-to-market strategies through insights gathered from the field.

⛳️ Requirements

• Demonstrated success in leading technical sales within complex B2B environments, preferably with multi-year committed-capacity or reserved-capacity deal frameworks.

• Proven history of recruiting, developing, and managing Solutions Architecture or Field Engineering teams.

• Technical expertise to lead intricate technical sales conversations while empowering others.

• Experience in establishing or managing production ML workloads, including distributed training and multi-node inference.

• Comprehensive understanding of performance bottlenecks across interconnects, memory bandwidth, I/O, and cluster scheduling.

• Proficiency with NVIDIA Hopper and Blackwell architectures, including H100/H200, GB200 NVL72, and B200.

• Knowledge of NVIDIA reference systems such as DGX, HGX, and MGX.

• In-depth understanding of NVLink/NVSwitch domains, InfiniBand, Spectrum-X Ethernet, RDMA/RoCE, and DPUs.

• Strongly preferred: pre-sales or infrastructure leadership experience at a NeoCloud, hyperscaler AI organization, or accelerated-hardware vendor.

• Practical experience with cloud-native orchestration for AI, including Kubernetes GPU operators and device plugins, KubeVirt, Metal3/Ironic bare-metal provisioning, and Slurm.

• Familiarity with high-throughput parallel/object storage systems for AI workflows.

• Awareness of data center physical constraints, such as power, cooling, and rack density.

🏝️ Benefits

• Employees have the option to work remotely.