AI Solutions Architect​/Field Application Engineer; FAE

Overview

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture.

We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.

Together, we advance your career.

The Role:

We are looking for an AI enthusiast with strong technical fundamentals and customer-facing aptitude to join AMD as an Entry-Level AI Solutions Architect / Field Application Engineer. This is a frontline, tip-of-the-spear role where you will work closely with customers, internal engineering teams, and ecosystem partners to help deploy, optimize, and scale AI and high-performance workloads on AMD CPU and GPU platforms.

• Customer & Field Engagement:
  Serve as a technical point of contact for customers, supporting AI and HPC workloads on AMD CPU and GPU platforms; work directly with customers to understand their use cases, requirements, and constraints, and guide them through solution design and deployment; deliver technical presentations, demos, and architecture walkthroughs to both technical and non-technical audiences; program-manage customer opportunities as they grow in complexity, coordinating activities across internal and external stakeholders.
• Technical Enablement & Hands-On Work:
  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 first-level profiling and analysis of applications to identify performance bottlenecks and optimization opportunities; support AI workloads such as training, inference, and data preprocessing across CPU and GPU platforms.
• Architecture & Platform Knowledge:
  Develop working knowledge of AMD CPU and GPU architectures and how they impact real-world workloads; understand full-stack solutions spanning hardware, system software, drivers, frameworks, and applications; assist in solution design for on-premises, cloud, and hybrid deployments.
• Cross-Functional Collaboration:
  
  Collaborate closely with engineering, product management, marketing, and sales teams to represent customer needs; provide structured feedback from the field to help influence product features, documentation, and roadmap decisions; contribute to internal knowledge sharing, best practices, and team initiatives.

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

• 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, including compute instance types and accelerators.
• Knowledge of containers and…