Senior GPU Data Center Architect for AI Infra

Applied Methods Ltd; Northern, New York (KY, NY); Hybrid; USD 160,000 - 290,000; Full time

Benefits offered by this job

Bonus program

Equity

Job summary

Applied Methods Ltd is seeking a Data Center Solutions Architect to design end-to-end GPU infrastructure for high-demand workloads across multiple locations such as Houston, New York, San Francisco and Seattle.

You will translate client requirements into scalable architectures, including GPU racks, network fabrics, storage pipelines, and management systems, while collaborating with engineering teams and guiding deployment to meet performance targets.

Qualifications

• Data center architecture and large-scale infrastructure experience.
• Strong networking knowledge: leaf/spine, high-performance fabrics.
• Familiarity with GPU systems and AI/ML workloads.
• Experience with storage systems and distributed infrastructure.
• Clear communication of complex topics to diverse audiences.
• 10+ years of relevant experience.

Responsibilities

• Engage with customers to capture requirements.
• Design full-stack data center solutions including GPU racks and network architecture.
• Define storage systems and data pipelines.
• Specify control plane and management systems.
• Collaborate with engineering teams to ensure feasibility.
• Support deployment planning and ensure performance targets.
• Act as trusted technical advisor to customers.
• Stay current with AI tooling to augment design processes.

Skills

Data center architecture

Networking (leaf/spine)

GPU AI infrastructure

Storage systems

Communication

Customer-facing

10+ years experience

Job description

Applied Methods Ltd is seeking a Data Center Solutions Architect to design end-to-end GPU infrastructure for high-demand workloads across multiple locations such as Houston, New York, San Francisco and Seattle.

You will translate client requirements into scalable architectures, including GPU racks, network fabrics, storage pipelines, and management systems, while collaborating with engineering teams and guiding deployment to meet performance targets.