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Principal Solutions Architect

NscaleoperationsukltdPosted 10 Oct 2026

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About this role

About Nscale 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.

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'll build trust through openness and transparency, where everyone is inspired to do their best work. If you join our team, you'll be contributing to building the technology that powers the future. About the Role We are hiring a Principal Solution Architect to design and deliver end-to-end GPU infrastructure solutions that power some of the world's most demanding AI workloads.

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. You'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'll also leverage modern AI tooling and automation to accelerate solution design, architecture validation, and deployment readiness. Working closely with customers and Nscale's Engineering, Infrastructure, and Deployment teams, you'll act as a trusted technical advisor, ensuring our solutions deliver exceptional performance, reliability, and scalability. What you'll be doing GPU Infrastructure & Solution Architecture Design end-to-end GPU infrastructure solutions tailored to customer requirements and AI workloads.

Architect GPU cluster configurations, including rack layouts, power and cooling considerations, and NVLink/NVSwitch domains. Design high-performance network architectures, including leaf/spine, rail-optimised fabrics, InfiniBand, Ethernet, RoCE, and GPUDirect RDMA. Define storage architectures and data pipelines supporting large-scale AI training and inference workloads. Design infrastructure and orchestration layers, including bare-metal provisioning, Kubernetes, Slurm, and multi-tenant scheduling.

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Architect control plane and management systems covering monitoring, alerting, and cluster lifecycle management. Customer Engagement & Technical Advisory Engage directly with customers to understand their technical requirements, workloads, and infrastructure challenges. Translate customer needs into detailed technical architectures, specifications, and reference designs. Act as a trusted technical advisor, guiding customers through infrastructure design decisions and technical trade-offs.

Collaborate with internal Engineering teams to ensure proposed solutions are technically feasible, scalable, and aligned with Nscale's infrastructure capabilities. Support deployment planning and ensure solutions meet agreed performance, reliability, and operational requirements. GPU Performance, Testing & Validation Define and execute infrastructure acceptance and validation plans for GPU clusters. Lead performance benchmarking using NCCL collectives and other GPU performance testing tools.

Validate network fabrics, GPU interconnects, and cluster configurations to ensure optimal performance. Troubleshoot multi-node GPU performance issues, including degraded links, fabric misconfigurations, stragglers, and performance regressions. Establish performance acceptance criteria and support GPU infrastructure sign-off before production deployment. AI-Driven Design & Infrastructure Automation Leverage AI tooling to automate and accelerate infrastructure design and engineering workflows.

Develop automated approaches for generating and iterating on architectures, bills of materials (BoMs), network topologies, and deployment artefacts. Apply LLM-assisted engineering, code generation, and agentic workflows to improve design efficiency and accuracy. Use Infrastructure-as-Code and scripting tools such as Terraform, Ansible, Python, or Go to streamline infrastructure design and deployment. Identify opportunities to improve repeatability, consistency, and scalability across solution architecture processes.

Storage, Reliability & Operational Readiness Design storage solutions optimised for AI workloads, including parallel and distributed file systems. Consider data throughput, checkpointing, and storage performance requirements when designing GPU clusters. Incorporate reliability, availability, and serviceability principles into infrastructure architectures. Define monitoring, health checks, observability, and diagnostic requirements for production environments.

Partner with Infrastructure and Operations teams to ensure successful deployment, validation, and ongoing operational readiness. Technology Research & Continuous Improvement Stay current with emerging GPU technologies, networking architectures, storage platforms, and AI infrastructure innovations. Research and evaluate new technologies to inform Nscale's future solution architectures. Contribute to the development of reusable reference architectures, engineering standards, and technical best practices.

Share technical knowledge and expertise across Engineering, Product, and customer-facing teams. About You Required Experience 10+ years of relevant experience in solution architecture, infrastructure engineering, high-performance computing, or large-scale data centre environments. Strong experience designing and delivering large-scale GPU infrastructure and AI/HPC solutions. Deep technical understanding across the full GPU infrastructure stack, from bare-metal compute and networking through to orchestration and workload management.

Proven ability to translate complex customer requirements into detailed technical architectures and implementation plans. Strong analytical and problem-solving skills, with the ability to troubleshoot complex infrastructure and performance challenges. Excellent communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences. GPU Infrastructure & Networking Strong understanding of GPU architectures, including NVLink/NVSwitch, PCIe, memory bandwidth, and GPUDirect RDMA.

Deep expertise in high-performance networking, including InfiniBand, RoCE, congestion control, leaf/spine architectures, and rail-optimised fabric design. Experience with bare-metal infrastructure provisioning, including PXE/iPXE, firmware management, and BMC technologies. Hands-on experience with Kubernetes and Slurm in production environments. Understanding of how network topology and GPU interconnect design impact distributed AI workload performance.

GPU Performance & Validation Hands-on experience with NCCL and collective communication benchmarking, including nccl-tests. Familiarity with NVIDIA performance validation and diagnostic tooling, including Fabric Manager, DCGM, nvidia-smi, and nvbandwidthtest. Experience with InfiniBand and Ethernet fabric diagnostics, including tools such as ibdiagnet. Proven ability to validate and troubleshoot multi-node GPU clusters, identifying performance bottlenecks, degraded links, fabric issues, and configuration problems.

Automation & Software Engineering Strong scripting and automation skills using Python, Go, or similar languages. Experience with Infrastructure-as-Code tools such as Terraform and Ansible. Demonstrated ability to automate engineering and design workflows using modern AI tooling. Familiarity with LLM-assisted development, code generation, and agentic automation pipelines. Storage, AI Workloads & Reliability Experience with storage technologies supporting AI workloads, including Lustre, GPFS, WEKA, BeeGFS, or large-scale NFS.

Understanding of distributed training and inference infrastructure requirements. Familiarity with AI/ML frameworks such as PyTorch, DeepSpeed, or Megatron-style training stacks. Knowledge of checkpointing, data pipeline design, and high-throughput storage architectures. Understanding of GPU cluster reliability, burn-in testing, heal

Source listing: arbeitnow_main

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