AI & Datacenter Solutions Architect

About the role

At AMD, we are seeking an AI & Datacenter Solutions Architect with strong technical fundamentals and a proven customer-facing track record to join our team. This role involves working closely with strategic datacenter, cloud, and emerging neocloud customers, internal engineering teams, and ecosystem partners to help deploy, optimize, and scale AI and high-performance workloads on AMD CPU and GPU platforms.

Responsibilities

• Serve as the senior technical point of contact for strategic customers across cloud, on-prem, and neocloud (GPU-specialized cloud) providers, supporting AI and HPC workloads on AMD CPU and GPU platforms.
• Work directly with customer engineering and leadership contacts to understand their use cases, requirements, and constraints, and guide them through solution design and deployment.
• Deliver persuasive technical presentations, demos, and architecture walkthroughs to both technical and executive audiences, translating AMD’s differentiation into business value.
• Program-manage customer opportunities as they grow in complexity, coordinating activities across internal and external stakeholders and keeping momentum through long sales cycles.
• Perform hands-on system bring-up including hardware installation, firmware configuration, OS installation, and driver setup.
• Deploy and validate open-source AI and HPC software stacks (e.g., Linux, ROCm, AI frameworks, containers).
• Run functionality, performance, and scalability benchmarks on CPU and GPU workloads.
• Perform profiling and analysis of applications to identify performance bottlenecks and optimization opportunities.
• Support AI/agentic workloads spanning training, inference, and data preprocessing across CPU and GPU platforms.
• Lead solution design for on-premises, cloud, neocloud, and hybrid deployments.
• Cross-Functional Collaboration:
• Collaborate closely with engineering, product management, marketing, and sales teams to represent customer needs and navigate decisions across organizational boundaries.
• Provide structured voice-of-the-customer feedback to influence product features, documentation, and roadmap decisions.
• Contribute to internal knowledge sharing, best practices, and team initiatives, and mentor teammates where your expertise intersects their challenges.

Qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent practical experience).
• Demonstrated success in a customer-facing technical role supporting compute, datacenter, or cloud customers.
• Strong interest in AI/ML technologies and proven ability to work across hardware and software layers.
• Hands-on experience with Linux-based systems.
• Programming experience in one or more of: Python, C/C++, Bash.
• Familiarity with AI frameworks or tools (e.g., PyTorch, TensorFlow, ONNX, Hugging Face, or similar).
• Strong communication skills with the ability to explain technical concepts clearly and persuasively.
• Ability to work effectively in a team-oriented, cross-functional environment.

Preferred Qualifications

• Experience working with GPU computing and/or accelerator-based workloads.
• Exposure to profiling and performance analysis tools for CPU and GPU workloads.
• Understanding of computer architecture concepts (CPU pipelines, memory hierarchy, GPU execution models).
• Experience setting up or working in a lab environment with servers, networking, and storage.
• Familiarity with cloud platforms such as AWS, Azure, Google Cloud, or Oracle Cloud — and/or neocloud GPU providers (e.g., CoreWeave, Lambda, Crusoe, Nebius) — including compute instance types and accelerators.
• Knowledge of containers and orchestration (Docker, Kubernetes).
• Prior success in customer-facing or field engineering roles within the datacenter or cloud ecosystem.