Head of AI Engineering Productivity, Global Cluster Engineering

Key Responsibilities

• Define and execute AMD’s AI enablement strategy across software engineering, hardware development, validation, operations, and business functions.
• Lead the adoption of AI copilots, autonomous agents, and intelligent workflows throughout the engineering lifecycle.
• Deploy agentic systems that autonomously triage issues, route work, and propose solutions across large-scale engineering environments.
• Build scalable AI platforms, APIs, frameworks, and services that accelerate safe and effective AI adoption across teams.
• Establish best practices for AI model integration, evaluation, deployment, governance, and operationalization.
• Develop reusable agent frameworks that support tool orchestration, memory management, workflow automation, and customization.
• Identify opportunities to replace manual processes with intelligent, AI-driven workflows that improve efficiency and scalability.
• Drive measurable improvements in engineering productivity, developer velocity, hardware validation cycles, and organizational effectiveness.
• Enable AI-powered capabilities such as documentation generation, onboarding assistants, knowledge retrieval, and workflow automation.
• Transform internal support functions through conversational agents capable of executing tasks across business operations.
• Partner with engineering, research, infrastructure, and product leaders to identify priorities and accelerate AI adoption.
• Collaborate with external partners, model providers, infrastructure vendors, and open-source communities to incorporate best-in-class technologies.
• Optimize AI platform performance, inference efficiency, and operational cost at scale.
• Evaluate emerging AI ecosystems, multimodal models, development tools, and agentic technologies to guide strategic investments.

Preferred Experience

• Deep expertise in AI/ML systems, Large Language Models (LLMs), agentic architectures, modern AI tooling, AI-native developer workflows, AI copilots, autonomous agents, and intelligent workflow systems.
• Hands-on experience with agent frameworks, tool use, orchestration systems, memory architectures, evaluation systems, model integration, model evaluation, and model deployment.
• Experience delivering platform-level products and production-ready AI solutions within large-scale engineering organizations.
• Expertise implementing intelligent automation solutions, including automated bug triage, issue routing, workflow automation, intelligent task execution, AI-powered documentation generation, onboarding assistants, knowledge retrieval systems, and conversational AI.
• Experience optimizing inference performance, AI infrastructure, scalability, operational efficiency, and deployment costs at enterprise scale.
• Knowledge of multimodal models, emerging AI ecosystems, open-source AI communities, agent technologies, and developer productivity tools.
• Proven track record delivering complex technology platforms at scale and driving cross-organizational transformation initiatives across engineering, research, product, infrastructure, and business organizations.
• Ability to operate at both strategic and hands-on levels while translating emerging AI capabilities into measurable business impact.