[Hiring] Neo-Cloud Solutions Architect @S Base Technologies

Role Description

100% remote 12-month contract for a Neo-Cloud Solutions Architect (with Data Center exp.) on a remote basis.

We're seeking an experienced Solutions Architect to bridge the gap between design and deployment of large-scale AI and HPC GPU infrastructure. This role serves as the primary technical point of contact for the customer throughout the full lifecycle of building next-generation GPU cloud infrastructure — driving end-to-end technology solution integration and sharing field feedback with business and engineering teams to help shape product strategy.

• Collaborate with the customer's cloud partners to design, implement, and operationalize innovative GPU hardware and software solutions.
• Partner with Sales Account Managers and other business leads to identify and secure business opportunities for GPU infrastructure products and solutions.
• Act as the primary technical point of contact for the customer through the full lifecycle of developing, building, and bringing large-scale GPU cloud infrastructure into production.
• Lead regular technical customer meetings covering project and product details, feature discussions, introductions to new technologies, and debugging sessions.
• Work with the customer to build proofs of concept addressing critical business needs across networking and compute infrastructure.
• Prepare and deliver technical content to the customer, including presentations and workshops.
• Analyze and develop joint solutions for customer performance and scaling issues.

Qualifications

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or another Engineering field, or equivalent experience.
• Motivation and skill to own and drive technical engagements with customers across the full customer lifecycle.
• 7+ years of Solution Engineering (or similar Sales Engineering / Cloud Engineering) experience working directly with partners and customers.
• Experience crafting and deploying large-scale cluster environments.
• Practical expertise in data center design, development, and execution for AI and HPC.
• Strong time management skills and the ability to balance multiple tasks, with clear communication through documents and presentations.

Requirements

• Practical familiarity with GPU hardware and networking components (Ethernet/InfiniBand), storage, and other elements of large-scale AI and HPC cluster environments.
• Practical knowledge of GPU systems management technologies such as NCCL, DCGM, UFM, Mission Control, and Base Command Manager.
• Background with at-scale GPU systems, including performance testing and AI benchmarking.
• Hands-on experience with cluster administration and orchestration (SLURM, Kubernetes).