Principal Solutions Architect

<h2 data-pm-slice=&quot;1 1 []&quot;>About Nscale</h2> <p>Nscale is the GPU cloud engineered for AI. We provide cost-effective, high-performance infrastructure for AI start-ups and large enterprise customers. Nscale enables AI-focused companies to achieve superior results by reducing the complexity of AI development. Our GPU cloud bolsters technical capabilities and directly supports strategic business outcomes, including cost management, rapid innovation, and environmental responsibility.</p> <p>We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As an Nscaler, you&#39;ll build trust through openness and transparency, where everyone is inspired to do their best work. If you join our team, you&#39;ll be contributing to building the technology that powers the future.</p> <h2>About the Role</h2> <p>We are hiring a <strong>Principal Solution Architect</strong> to design and deliver end-to-end GPU infrastructure solutions that power some of the world&#39;s most demanding AI workloads.</p> <p>This is a highly technical, customer-facing role at the intersection of AI infrastructure, solution architecture, and engineering. You will translate complex customer requirements into scalable, high-performance infrastructure designs spanning GPU compute, networking, storage, data centre systems, and orchestration platforms.</p> <p>You&#39;ll own solution designs end-to-end, from bare-metal GPU fabrics and high-performance networking through to the orchestration layers customers use to run their AI workloads. You&#39;ll also leverage modern AI tooling and automation to accelerate solution design, architecture validation, and deployment readiness.</p> <p>Working closely with customers and Nscale&#39;s Engineering, Infrastructure, and Deployment teams, you&#39;ll act as a trusted technical advisor, ensuring our solutions deliver exceptional performance, reliability, and scalability.</p> <h2>What you&#39;ll be doing</h2> <h3>GPU Infrastructure &amp; Solution Architecture</h3> <ul data-spread=&quot;false&quot;> <li> <p>Design end-to-end GPU infrastructure solutions tailored to customer requirements and AI workloads.</p> </li> <li> <p>Architect GPU cluster configurations, including rack layouts, power and cooling considerations, and NVLink/NVSwitch domains.</p> </li> <li> <p>Design high-performance network architectures, including leaf/spine, rail-optimised fabrics, InfiniBand, Ethernet, RoCE, and GPUDirect RDMA.</p> </li> <li> <p>Define storage architectures and data pipelines supporting large-scale AI training and inference workloads.</p> </li> <li> <p>Design infrastructure and orchestration layers, including bare-metal provisioning, Kubernetes, Slurm, and multi-tenant scheduling.</p> </li> <li> <p>Architect control plane and management systems covering monitoring, alerting, and cluster lifecycle management.</p> </li> </ul> <h3>Customer Engagement &amp; Technical Advisory</h3> <ul data-spread=&quot;false&quot;> <li> <p>Engage directly with customers to understand their technical requirements, workloads, and infrastructure challenges.</p> </li> <li> <p>Translate customer needs into detailed technical architectures, specifications, and reference designs.</p> </li> <li> <p>Act as a trusted technical advisor, guiding customers through infrastructure design decisions and technical trade-offs.</p> </li> <li> <p>Collaborate with internal Engineering teams to ensure proposed solutions are technically feasible, scalable, and aligned with Nscale&#39;s infrastructure capabilities.</p> </li> <li> <p>Support deployment planning and ensure solutions meet agreed performance, reliability, and operational requirements.</p> </li> </ul> <h3>GPU Performance, Testing &amp; Validation</h3> <ul data-spread=&quot;false&quot;> <li> <p>Define and execute infrastructure acceptance and validation plans for GPU clusters.</p> </li> <li> <p>Lead performance benchmarking using NCCL collectives and other GPU performance testing tools.</p> </li> <li> <p>Validate network fabrics, GPU interconnects, and cluster configurations to ensure optimal performance.</p> </li> <li> <p>Troubleshoot multi-node GPU performance issues, including degraded links, fabric misconfigurations, stragglers, and performance regressions.</p> </li> <li> <p>Establish performance acceptance criteria and support GPU infrastructure sign-off before production deployment.</p> </li> </ul> <h3>AI-Driven Design &amp; Infrastructure Automation</h3> <ul data-spread=&quot;false&quot;> <li> <p>Leverage AI tooling to automate and accelerate infrastructure design and engineering workflows.</p> </li> <li> <p>Develop automated approaches for generating and iterating on architectures, bills of materials (BoMs), network topologies, and deployment artefacts.</p> </li> <li> <p>Apply LLM-assisted engineering, code generation, and agentic workflows to improve design efficiency and accuracy.</p> </li> <li> <p>Use Infrastructure-as-Code and scripting tools such as Terraform, Ansible, Python, or Go to streamline infrastructure design and deployment.</p> </li> <li> <p>Identify opportunities to improve repeatability, consistency, and scalability across solution architecture processes.</p> </li> </ul> <h3>Storage, Reliability &amp; Operational Readiness</h3> <ul data-spread=&quot;false&quot;> <li> <p>Design storage solutions optimised for AI workloads, including parallel and distributed file systems.</p> </li> <li> <p>Consider data throughput, checkpointing, and storage performance requirements when designing GPU clusters.</p> </li> <li> <p>Incorporate reliability, availability, and serviceability principles into infrastructure architectures.</p> </li> <li> <p>Define monitoring, health checks, observability, and diagnostic requirements for production environments.</p> </li> <li> <p>Partner with Infrastructure and Operations teams to ensure successful deployment, validation, and…