Solutions Architect

Job Specification: Solution Architect - NVIDIA Cluster (End-to-End Design & Validation)

Travel: Occasional travel to datacenter sites outside the UK

Engagement: Contract Inside IR35

Department: Engineering/Advanced Compute

Role Overview

We are seeking a highly skilled Solution Architect with deep experience designing, validating, and delivering end-to-end NVIDIA GPU clusters in enterprise and hyperscale environments. This individual will own the full lifecycle of architectural design-from requirements gathering through implementation oversight and performance validation. They will work closely with engineering, networking, DevOps, security, and datacenter operations teams to ensure high-performance, scalable, and resilient GPU infrastructure for AI, HPC, and ML workloads.

The role is primarily London-based one day per week, with occasional international travel required to support datacenter design reviews, deployment validation, or site acceptance testing.

Key Responsibilities

• Lead the architecture of NVIDIA GPU clusters leveraging technologies such as H100/H200, NVLink, NVSwitch, DGX, HGX, or SuperPod-class designs.
• Produce high-level and low-level designs (HLD/LLD), including compute, network, storage, and power/cooling considerations.
• Validate hardware and platform selections, ensuring alignment with customer requirements and scalability goals.
• Design fabric architectures including InfiniBand (200/400Gb), RoCE, and high-performance east-west traffic patterns.
• Define and execute validation test plans for GPU cluster performance, resilience, networking throughput, and workload behaviour.
• Oversee integration of GPU nodes, networking, and storage systems into the existing datacenter environment.
• Collaborate with DevOps/Platform teams to validate cluster orchestration (Kubernetes, Slurm, Bright Cluster Manager, or equivalents).
• Validate firmware, drivers, NCCL, CUDA libraries, and container environments for production readiness.

Deployment & Delivery Oversight

• Provide technical leadership across the full deployment lifecycle.
• Partner with datacenter operations to ensure correct rack layouts, cabling, airflow and power design.
• Support delivery teams during build-out phases, ensuring the design is executed correctly.
• Participate in factory acceptance tests (FAT), site acceptance tests (SAT), and operational readiness reviews.

Stakeholder Collaboration

• Work closely with internal and external teams including network engineering, platform engineering, procurement, and vendors such as NVIDIA, Mellanox, Supermicro, Dell, or HPE.
• Provide technical guidance to customers, partners, and cross-functional engineering teams.
• Communicate complex architectural concepts clearly to both technical and non-technical audiences.

Documentation & Governance

• Produce detailed architecture documents, diagrams, acceptance criteria, and operational runbooks.
• Ensure security, compliance, and governance standards are built into the design.
• Provide knowledge transfer and training sessions to internal teams where required.

Required Skills & Experience

• Proven experience architecting and delivering NVIDIA GPU clusters at scale (AI/ML/HPC environments).
• Strong hands-on understanding of GPU interconnects (NVLink/NVSwitch) and DGX/HGX/SuperPod architectures.
• Deep knowledge of InfiniBand and high-performance networking architectures.
• Experience with cluster orchestration: Kubernetes, Slurm, PBS, or similar.
• Familiarity with AI/ML workload requirements, CUDA, Docker/OCI containers, and NVIDIA software stacks (NCCL, CUDA Toolkit).
• Comfort with Linux systems engineering, hardware validation, and troubleshooting across compute/network layers.

Soft Skills

• Strong communication skills, with the ability to bridge engineering and business discussions.
• Comfortable owning architecture decisions and delivering executive-ready documentation.
• Ability to work autonomously while coordinating with multi-disciplinary teams.
• Problem-solver with strong critical-thinking abilities and a delivery-focused mindset.
• Experience with hyperscaler-class deployments or multi-megawatt datacenter environments.
• Work with NVIDIA Base Command Manager or similar cluster management tooling.
• Exposure to data pipelines, storage systems (Lustre, GPUDirect Storage, Ceph), or AI workflow platforms.
• Certifications such as NVIDIA Certified Associate/Expert, Kubernetes certifications (CKA/CKS), or related vendor accreditations.

What We Offer

• Hybrid working: 1 day per week in London.
• Opportunity to design next-generation high-performance GPU infrastructure.
• Exposure to cutting-edge AI compute at scale.

Job Details

• Seniority level: Mid-Senior level
• Employment type: Contract
• Job function: Information Technology
• Industries: Professional Services, IT Services, IT Consulting

• Proven experience architecting and delivering NVIDIA GPU clusters at scale (AI/ML/HPC environments).
• Strong hands-on understanding of GPU interconnects (NVLink/NVSwitch) and DGX/HGX/SuperPod architectures.
• Deep knowledge of InfiniBand and high-performance networking architectures.
• Experience with cluster orchestration: Kubernetes, Slurm, PBS, or similar.
• Familiarity with AI/ML workload requirements, CUDA, Docker/OCI containers, and NVIDIA software stacks (NCCL, CUDA Toolkit).
• Comfort with Linux systems engineering, hardware validation, and troubleshooting across compute/network layers.

Soft Skills

• Strong communication skills, with the ability to bridge engineering and business discussions.
• Comfortable owning architecture decisions and delivering executive-ready documentation.
• Ability to work autonomously while coordinating with multi-disciplinary teams.
• Problem-solver with strong critical-thinking abilities and a delivery-focused mindset.
• Experience with hyperscaler-class deployments or multi-megawatt datacenter environments.
• Work with NVIDIA Base Command Manager or similar cluster management tooling.
• Exposure to data pipelines, storage systems (Lustre, GPUDirect Storage, Ceph), or AI workflow platforms.
• Certifications such as NVIDIA Certified Associate/Expert, Kubernetes certifications (CKA/CKS), or related vendor accreditations.