Senior Solutions Architect

Senior Solutions Architect, Post-Sales (EMEA) – AI/ML Infrastructure - EMEA Remote Role:A high-growth GPU cloud and AI infrastructure platform provider is looking for a Senior Solutions Architect to join its EMEA post-sales team. This is a customer-facing, highly technical role helping enterprise clients design, deploy, and scale production AI/ML workloads on a Kubernetes-native, GPU-accelerated Platform-as-a-Service. You'll sit at the intersection of platform engineering, MLOps, and infrastructure strategy, acting as a trusted technical advisor throughout the customer lifecycle. You will be one of the primary technical voices shaping how enterprise customers architect and scale some of the most demanding AI infrastructure workloads in the market today, working directly with cutting-edge GPU fabric and large-scale distributed training environments.This is a business riding the wave of explosive demand for AI compute, offering genuine scale, investment, and a seat at the table as the platform's capabilities and customer base expand rapidly.You'll join a small, senior Solutions Architecture function with real autonomy, direct access to product and engineering leadership, and the flexibility of a role built around trust rather than micromanagement. Responsibilities: Design end-to-end AI/ML platform architectures spanning inference, training, and data pipelines for enterprise customersDevelop reference architectures for GPU cluster deployment, LLM serving, and multi-tenant ML infrastructureAdvise on GPU fabric topology, including NVLink, InfiniBand, and RoCEv2, for distributed training environmentsAct as the primary technical advisor and escalation point for assigned customers, leading root cause analysis on complex production issuesDeliver technical workshops, proof-of-concept engagements, and executive-level presentations on AI infrastructure strategyDesign observability strategies across DCGM, OpenTelemetry, eBPF, and GPU metrics pipelinesPartner with customer platform, MLOps, and data science stakeholders to translate workload requirements into scalable architectureFeed customer insights back into the product and engineering roadmap, and mentor junior members of the Solutions Architecture team Skills/Must have: Experience: 8+ years in infrastructure, platform, or solutions engineering, including 3+ years focused specifically on AI/ML infrastructure or MLOpsCore Tech/Domain: Deep, hands-on Kubernetes expertise (cluster lifecycle, workloads, operators, RBAC) plus direct experience with NVIDIA GPU infrastructure (H100/H200/B200 preferred)Methodology/Protocols: Working knowledge of distributed training concepts (NCCL, tensor and pipeline parallelism), LLM inference serving and optimisation (vLLM, NIM, TGI), and GPU networking (GPU Operator, MIG, SR-IOV, IB/RoCEv2)Soft Skills: Confident, credible communicator able to run technical discussions with engineers through to executive stakeholders; strong troubleshooting mindset and customer advisory presence Nice to haves: Experience with Run:AI or Slurm for GPU scheduling and workload optimisationFamiliarity with PyTorch or TensorFlowPublic cloud experience (AWS, Azure, or GCP) across networking, IAM, and managed KubernetesCertifications such as CKA, CKAD, AWS Solutions Architect, Azure Solutions Architect, or GCP Professional Cloud ArchitectUnderstanding of multi-tenant GPU isolation (SR-IOV VFs, DPU offload) Benefits: Competitive base salary with performance-related bonusRemote-first/flexible working across EMEAPrivate healthcare and pension contributionTraining and certification allowanceGenuine exposure to cutting-edge GPU and AI infrastructure at enterprise scale Salary:£150,000