ଚାକିରି
ପାର୍ଟନର ଫିଡ୍ ରୁ
Solution Architect - GPU & HPC
ଅନୁରୋଧ ଅନୁଯାୟୀ ମୂଲ୍ୟ
ବିବରଣୀ
- ନିଯୁକ୍ତି ପ୍ରକାର
- ପୂର୍ଣ୍ଣ ସମୟ
- ରିମୋଟ୍
- ନା
- କମ୍ପାନୀ
- Nexgencloud
ବିବରଣୀ
Solutions Architect
Location: UK-based — Customer-Site Travel Required
ABOUT NEXGEN CLOUD
NexGen Cloud is the company behind Hyperstack, a full-stack AI cloud serving tens of thousands of customers from AI researchers to enterprises running the world’s most compute-intensive workloads. We deliver on-demand and private GPU infrastructure to teams who treat performance as a requirement, not a feature.
We’re a tight-knit, fast-moving team working at the cutting edge of AI cloud infrastructure. We practise what we preach, equipping our people with AI at every level so we can solve harder problems, ship faster, and keep raising the bar for what enterprise GPU infrastructure looks like.
THE ROLE: Solutions Architect
This role exists because our pipeline of technically complex, high-value GPU cloud opportunities is growing — and winning them requires more than a great sales team. The Solutions Architect sits at the intersection of sales, infrastructure, and the customer, translating complex workload requirements into technically sound, commercially viable solutions on the Hyperstack platform.
You’ll be the primary technical authority through the sales cycle: engaging directly with prospective and existing customers, producing detailed solution designs, and ensuring that what is proposed can actually be delivered to the standard committed. This is a role for someone who is equally comfortable in a customer meeting and a technical design review — someone who earns trust through depth, not slides.
WHAT YOU’LL BE DOING
Rather than a long checklist, here’s what success in this role looks like:
• Own the technical sales cycle end-to-end — from initial customer brief through architecture design, proposal, and delivery handover — acting as the primary technical authority for GPU cloud solution design.
• Engage directly with prospective and existing customers to understand workload requirements, technical constraints, and commercial objectives, producing detailed solution designs including architecture diagrams, network topology, storage configurations, and GPU resource allocation models.
• Collaborate closely with Pre-Sales Engineering, Data Centre, Infrastructure, and Network Engineering teams to validate delivery feasibility before commitments are made to customers.
• Build and maintain a library of reference architectures and solution templates spanning AI/ML training, inference, HPC, and rendering workloads — accelerating the sales cycle and improving proposal consistency across the team.
• Develop high-quality technical proposals, RFP responses, and statements of work, supporting commercial discussions with clear scoping, realistic estimates, and honest risk assessments.
• Define and maintain comprehensive Bills of Materials (BoMs) for all proposed solutions, ensuring accuracy for procurement, provisioning, and margin review.
• Feed back recurring customer requirements and competitive intelligence to engineering leadership, directly influencing the Hyperstack product roadmap.
ABOUT YOU
We’re more interested in how you think and work than in a perfect CV. You’ll likely come from an HPC, AI infrastructure, or high-performance cloud background and bring a combination of the following:
Essential
• Proven experience in HPC or AI software stack design and delivery at scale — including workload profiling, scheduler configuration (SLURM, PBS, or equivalent), MPI/NCCL tuning, and distributed training frameworks such as PyTorch, JAX, or DeepSpeed.
• Deep understanding of GPU software environments: CUDA, cuDNN, NCCL, driver stacks, and the tooling required to run large-scale AI training and inference workloads reliably in production.
• Hands-on experience optimising AI and HPC workloads across multi-GPU and multi-node configurations — including profiling, bottleneck identification, and performance tuning at both the application and infrastructure layer.
• Strong working knowledge of containerisation and orchestration in HPC/AI contexts: Docker, Kubernetes, NVIDIA GPU Operator, and container-native workload management.
• Background in an OEM, hyperscaler, neo-cloud, or enterprise/research HPC environment, with demonstrable exposure to the full design-to-deployment lifecycle for GPU-accelerated workloads.
• Ability to produce clear, professional technical documentation and architecture diagrams suitable for both engineering and board-level audiences.
• Confident engaging with customers, vendors, and internal engineering teams as a technical authority — able to translate complex software and performance trade-offs into clear, actionable decisions.
Nice to Have
• Experience with large-scale cluster performance benchmarking — NCCL tests, MLPerf, or equivalent — and familiarity with what good looks like across different GPU generations and topologies.
• Exposure to MLOps tooling and AI platform layers: experiment tracking (MLflow, W&B), model serving frameworks (Triton, vLLM), and pipeline orchestration (Kubeflow, Airflow).
• Familiarity with InfiniBand and high-performance networking as it relates to distributed training performance — sufficient to engage credibly with network and infrastructure teams on topology and tuning decisions.
• Commercial awareness: experience contributing to BoMs, technical proposals, or RFP responses in a pre-sales or customer-facing technical role.
WHAT WE OFFER
• Competitive salary and annual discretionary bonus scheme.
• Employee wellbeing benefits.
• 25 days of holiday, plus public holidays.
• Flexible working arrangements, with regular customer-site travel as part of the role.
• Real ownership and autonomy — you’ll have direct influence over the technical win rate on some of our most strategically important opportunities.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/nexgencloud/solution-architect-gpu-hpc-london-378815)
Location: UK-based — Customer-Site Travel Required
ABOUT NEXGEN CLOUD
NexGen Cloud is the company behind Hyperstack, a full-stack AI cloud serving tens of thousands of customers from AI researchers to enterprises running the world’s most compute-intensive workloads. We deliver on-demand and private GPU infrastructure to teams who treat performance as a requirement, not a feature.
We’re a tight-knit, fast-moving team working at the cutting edge of AI cloud infrastructure. We practise what we preach, equipping our people with AI at every level so we can solve harder problems, ship faster, and keep raising the bar for what enterprise GPU infrastructure looks like.
THE ROLE: Solutions Architect
This role exists because our pipeline of technically complex, high-value GPU cloud opportunities is growing — and winning them requires more than a great sales team. The Solutions Architect sits at the intersection of sales, infrastructure, and the customer, translating complex workload requirements into technically sound, commercially viable solutions on the Hyperstack platform.
You’ll be the primary technical authority through the sales cycle: engaging directly with prospective and existing customers, producing detailed solution designs, and ensuring that what is proposed can actually be delivered to the standard committed. This is a role for someone who is equally comfortable in a customer meeting and a technical design review — someone who earns trust through depth, not slides.
WHAT YOU’LL BE DOING
Rather than a long checklist, here’s what success in this role looks like:
• Own the technical sales cycle end-to-end — from initial customer brief through architecture design, proposal, and delivery handover — acting as the primary technical authority for GPU cloud solution design.
• Engage directly with prospective and existing customers to understand workload requirements, technical constraints, and commercial objectives, producing detailed solution designs including architecture diagrams, network topology, storage configurations, and GPU resource allocation models.
• Collaborate closely with Pre-Sales Engineering, Data Centre, Infrastructure, and Network Engineering teams to validate delivery feasibility before commitments are made to customers.
• Build and maintain a library of reference architectures and solution templates spanning AI/ML training, inference, HPC, and rendering workloads — accelerating the sales cycle and improving proposal consistency across the team.
• Develop high-quality technical proposals, RFP responses, and statements of work, supporting commercial discussions with clear scoping, realistic estimates, and honest risk assessments.
• Define and maintain comprehensive Bills of Materials (BoMs) for all proposed solutions, ensuring accuracy for procurement, provisioning, and margin review.
• Feed back recurring customer requirements and competitive intelligence to engineering leadership, directly influencing the Hyperstack product roadmap.
ABOUT YOU
We’re more interested in how you think and work than in a perfect CV. You’ll likely come from an HPC, AI infrastructure, or high-performance cloud background and bring a combination of the following:
Essential
• Proven experience in HPC or AI software stack design and delivery at scale — including workload profiling, scheduler configuration (SLURM, PBS, or equivalent), MPI/NCCL tuning, and distributed training frameworks such as PyTorch, JAX, or DeepSpeed.
• Deep understanding of GPU software environments: CUDA, cuDNN, NCCL, driver stacks, and the tooling required to run large-scale AI training and inference workloads reliably in production.
• Hands-on experience optimising AI and HPC workloads across multi-GPU and multi-node configurations — including profiling, bottleneck identification, and performance tuning at both the application and infrastructure layer.
• Strong working knowledge of containerisation and orchestration in HPC/AI contexts: Docker, Kubernetes, NVIDIA GPU Operator, and container-native workload management.
• Background in an OEM, hyperscaler, neo-cloud, or enterprise/research HPC environment, with demonstrable exposure to the full design-to-deployment lifecycle for GPU-accelerated workloads.
• Ability to produce clear, professional technical documentation and architecture diagrams suitable for both engineering and board-level audiences.
• Confident engaging with customers, vendors, and internal engineering teams as a technical authority — able to translate complex software and performance trade-offs into clear, actionable decisions.
Nice to Have
• Experience with large-scale cluster performance benchmarking — NCCL tests, MLPerf, or equivalent — and familiarity with what good looks like across different GPU generations and topologies.
• Exposure to MLOps tooling and AI platform layers: experiment tracking (MLflow, W&B), model serving frameworks (Triton, vLLM), and pipeline orchestration (Kubeflow, Airflow).
• Familiarity with InfiniBand and high-performance networking as it relates to distributed training performance — sufficient to engage credibly with network and infrastructure teams on topology and tuning decisions.
• Commercial awareness: experience contributing to BoMs, technical proposals, or RFP responses in a pre-sales or customer-facing technical role.
WHAT WE OFFER
• Competitive salary and annual discretionary bonus scheme.
• Employee wellbeing benefits.
• 25 days of holiday, plus public holidays.
• Flexible working arrangements, with regular customer-site travel as part of the role.
• Real ownership and autonomy — you’ll have direct influence over the technical win rate on some of our most strategically important opportunities.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/nexgencloud/solution-architect-gpu-hpc-london-378815)
ଏହି ତାଲିକା ଏକ ପାର୍ଟନର ଫିଡ୍ରୁ ଆସିଛି। ଉତ୍ସ ୱେବସାଇଟ୍ରେ ଆବେଦନ କରନ୍ତୁ।
ଉତ୍ସ: Nexgencloud
ଅବସ୍ଥାନ
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Nexgencloud ଦ୍ୱାରା ପ୍ରଦତ୍ତ ତାଲିକା।
