Cluster Solution Architect

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Cluster Solution Architect

About Our Team

Lenovo's Cloud Service Provider (CSP) team designs AI infrastructure solutions for hyperscalers, NeoCloud providers, and other data center customers. We bring together GPU platforms, high-performance networking, cluster software, and data center infrastructure to support large-scale AI training and inference.

As a Cluster Solutions Architect, you'll translate customer requirements into integrated, scalable solutions and compelling technical proposals. You'll collaborate with customers, sales teams, technology partners, and engineering teams to shape architectures that balance performance, efficiency, operational requirements, and deployment feasibility.

Ideal candidates will bring experience designing, architecting, and supporting large-scale AI infrastructure solutions in customer-facing environments, including GPU cluster deployments, AI inference platforms, and integrated hardware and software solutions for enterprise, cloud, hyperscale, or data center customers.

What You'll Do

- Design and optimize end-to-end AI inference and AI training solutions, with a primary emphasis on AI inference architecture, software stack integration, and production deployment of large-scale GPU cluster environments using NVIDIA software and platform technologies, including NVIDIA AI Enterprise, Dynamo, NIM, NVIDIA Infrastructure Controller (NICo), Mission Control, LPx, and emerging inference acceleration capabilities.
- Evaluate architecture tradeoffs to improve token latency, throughput, GPU utilization, and overall AI service efficiency.
- Translate customer workload, capacity, networking, power, and cooling requirements into integrated cluster architectures and technical recommendations.
- Design integrated AI cluster solutions that combine GPU compute platforms, high-performance networking, software stack components, storage, and supporting data center infrastructure into scalable customer-ready architectures.
- Provide technical leadership and architecture guidance throughout the customer solution lifecycle, including requirements gathering, solution design, technical reviews, proposal development, and customer presentations.
- Develop and deliver technical presentations, proofs of concept (POCs), demonstrations, and customer-facing solution proposals that clearly articulate architecture decisions, business value, technical differentiation, and deployment considerations.
- Support customer engagements by evaluating technical requirements and recommending scalable AI cluster architectures that balance performance, operational efficiency, deployment feasibility, and cost considerations.
- Prepare and contribute to requests for proposal (RFPs) and requests for quotation (RFQs), including technical compliance responses, solution documentation, bills of materials (BOMs), and customer-facing architecture content.
- Partner with engineering, deployment, hardware, networking, and supply chain teams to validate proposed solutions and confirm technical feasibility.
- Mentor junior RFP contributors and sales engineers, providing guidance on customer requirements, solution design, and common architecture tradeoffs.
- Develop best practices, reusable templates, reference architectures, sizing guidelines, and technical response content to improve proposal quality and response speed.
- Stay current on GPU roadmaps, high-speed networking, liquid cooling, cluster management, AI software ecosystems, inference optimization technologies, and emerging data center technologies.

Basic Qualifications

- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related engineering field; candidates with other educational backgrounds will also be considered if they demonstrate sufficient relevant technical knowledge.
- 10+ years of experience in data center infrastructure, solution architecture, systems engineering, pre-sales engineering, or technical consulting.
- Experience with large-scale AI inference and AI training workloads, including large language model (LLM) infrastructure, model serving environments, capacity planning, performance optimization, and production deployment of AI solutions.
- Experience designing, supporting, or deploying customer-facing AI infrastructure solutions, GPU clusters, high-performance computing environments, or large-scale data center architectures.
- Strong understanding of AI software stacks, cluster management environments, and infrastructure integration, with the ability to translate customer 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 solution designs.
- Strong knowledge of high-performance networking for AI clusters, including InfiniBand, RDMA over Converged Ethernet (RoCE), network operating systems, and deployment and discovery protocols such as 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, coolant distribution units (CDUs), high-density rack design, power distribution units (PDUs), busway systems, and AI data center power and cooling constraints.
- Familiarity with Python or another scripting language, automation tools, and infrastructure as code to support 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.

Preferred Qualifications

- Master's degree or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related engineering field.
- 5+ 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, including Linux, Slurm, Kubernetes, Docker, NVIDIA Infrastructure Controller (NICo), NVIDIA Mission Control, ROCm, or related tools, with experience deploying and managing production AI inference environments 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, high-performance computing environments, or large-scale data center solutions.
- Familiarity with hyperscaler data center infrastructure and operating environments, with demonstrated experience supporting hyperscaler, NeoCloud, or other data center infrastructure customers.
- Demonstrated experience developing technical proposals, architecture documentation, statements of work, solution recommendations, or customer-facing technical deliverables for complex infrastructure projects.
- Strong written communication skills, with the ability to translate complex technical requirements into clear, accurate customer-facing proposals and architecture documentation.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.