AI-Driven Data Center Solutions Architect (GPU Infra)

Applied Methods Ltd; Houston, New York, San Francisco, Seattle (TX, NY, CA, WA); Hybrid; USD 160,000 - 290,000; Full time

Job summary

Applied Methods Ltd. seeks a Data Center Solutions Architect to design and deliver end-to-end GPU infrastructure solutions for enterprise customers. The role spans network, compute, storage, and data center systems, translating customer requirements into detailed architectures and specifications.

You will engage with customers, guide deployment planning, and ensure performance targets are met. Ideal candidates bring 10+ years of relevant experience, deep knowledge of leaf/spine networks,

Qualifications

• Extensive experience in designing data center networks and compute/storage fabrics.
• Ability to translate customer requirements into scalable architectures.
• Strong communication skills for customer-facing engagements.
• 10+ years of relevant architecture experience in enterprise data centers.

Responsibilities

• Engage with customers to capture technical requirements and workloads.
• Design end-to-end data center solutions: GPU racks, network, storage, data pipelines.
• Translate needs into detailed architectures and specs for deployment.
• Collaborate with internal teams to ensure feasibility and alignment.
• Support deployment planning and ensure performance targets are met.
• Act as a trusted technical advisor to customers.
• Continuously explore and research new AI/ML infra technologies.

Skills

Data center architecture

Networking (leaf/spine)

GPU/AI infrastructure

Storage systems

Customer-facing

Communication

Large-scale infra experience

Job description

Applied Methods Ltd. seeks a Data Center Solutions Architect to design and deliver end-to-end GPU infrastructure solutions for enterprise customers. The role spans network, compute, storage, and data center systems, translating customer requirements into detailed architectures and specifications.

You will engage with customers, guide deployment planning, and ensure performance targets are met. Ideal candidates bring 10+ years of relevant experience, deep knowledge of leaf/spine networks,