Cluster Solution Architect

Lenovo • Morrisville, NC

4d • Onsite

About The Position

Lenovo's Cloud Service Provider (CSP) team designs AI infrastructure solutions for hyperscalers, NeoCloud providers, and other data center customers. The team combines GPU platforms, high-performance networking, cluster software, and data center infrastructure to support large-scale AI training and inference. The Cluster Solution Architect translates customer requirements into integrated, scalable solutions and technical proposals, collaborating with customers, sales teams, technology partners, and engineering teams to shape architectures that balance performance, efficiency, operational requirements, and deployment feasibility.

The role focuses on designing and optimizing end-to-end AI training and inference solutions, with particular emphasis on AI inference architecture, software stack integration, and production deployment of large-scale GPU cluster environments using NVIDIA technologies. It provides technical leadership throughout customer engagements, from requirements gathering and architecture design to proposal development, technical reviews, presentations, proofs of concept, and deployment feasibility validation. The architect also contributes reusable architectures, templates, sizing guidance, and proposal content, and mentors junior RFP contributors and sales engineers.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related engineering field; other educational backgrounds may be considered with sufficient relevant technical knowledge.
• 10 or more years of experience in data center infrastructure, solution architecture, systems engineering, pre-sales engineering, or technical consulting.
• Experience with large-scale AI inference and training workloads, including LLM infrastructure, model serving, capacity planning, performance optimization, and production deployment.
• Experience designing, supporting, or deploying customer-facing AI infrastructure solutions, GPU clusters, HPC environments, or large-scale data center architectures.
• Strong understanding of AI software stacks, cluster management environments, and infrastructure integration, with ability to translate business and technical requirements into deployable solutions.
• Experience working directly with customers, technical stakeholders, sales teams, or solution engineering organizations to develop and validate technical designs.
• Strong knowledge of AI-cluster high-performance networking, including InfiniBand, RoCE, network operating systems, ZTP, DHCP, LLDP, and SNMP.
• Familiarity with data center networking, high-speed optical interconnects, transceiver and fiber form factors, and interoperability requirements.
• Knowledge of liquid cooling, CDUs, high-density rack design, PDUs, busway systems, and AI data center power and cooling constraints.
• Familiarity with Python or another scripting language, automation tools, and infrastructure as code for solution validation and design.
• Willingness to travel up to 10% for customer meetings, technical workshops, partner engagements, and industry events.
• Commitment to continuously learning new GPU, networking, and data center technologies.

Nice To Haves

• Master's degree or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related engineering field.
• Five or more years of experience preparing or contributing to RFP/RFQ responses, including technical compliance, solution documentation, BOM development, and customer-facing architecture content, ideally for NVIDIA HGX or DGX platforms.
• Working knowledge of cluster software and management environments such as Linux, Slurm, Kubernetes, Docker, NVIDIA Infrastructure Controller (NICo), NVIDIA Mission Control, ROCm, or related tools; production AI inference deployment and management experience is preferred.
• Deep understanding of modern GPU server architectures, including NVIDIA and/or AMD accelerator-based systems.
• Experience supporting organizations that design, manufacture, integrate, or deploy enterprise AI infrastructure, GPU-based computing platforms, HPC environments, or large-scale data center solutions.
• Familiarity with hyperscaler data center infrastructure and operating environments, with experience supporting hyperscaler, NeoCloud, or other data center infrastructure customers.
• Experience developing technical proposals, architecture documentation, statements of work, solution recommendations, or customer-facing technical deliverables for complex infrastructure projects.
• Strong written communication skills and ability to translate complex technical requirements into clear customer-facing proposals and architecture documentation.

Responsibilities

• Design and optimize end-to-end AI inference and training solutions, emphasizing inference architecture, software stack integration, and production deployment of large-scale GPU clusters using NVIDIA technologies.
• Evaluate architecture tradeoffs to improve token latency, throughput, GPU utilization, and AI service efficiency.
• Translate customer workload, capacity, networking, power, and cooling requirements into integrated cluster architectures and technical recommendations.
• Design scalable AI cluster solutions combining GPU compute, high-performance networking, software, storage, and data center infrastructure.
• Provide technical leadership through requirements gathering, solution design, technical reviews, proposal development, and customer presentations.
• Develop and deliver technical presentations, proofs of concept, demonstrations, and customer-facing proposals that explain architecture decisions, business value, technical differentiation, and deployment considerations.
• Evaluate customer technical requirements and recommend scalable architectures balancing performance, operational efficiency, feasibility, and cost.
• Prepare and contribute to RFPs and RFQs, including technical compliance responses, solution documentation, bills of materials, and architecture content.
• Partner with engineering, deployment, hardware, networking, and supply chain teams to validate proposed solutions and technical feasibility.
• Mentor junior RFP contributors and sales engineers on customer requirements, solution design, and architecture tradeoffs.
• Develop best practices, reusable templates, reference architectures, sizing guidelines, and technical response content.
• Stay current on GPU roadmaps, high-speed networking, liquid cooling, cluster management, AI software ecosystems, inference optimization, and emerging data center technologies.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior