below marketmost similar roles pay hereabove market
This role pays more than
81%
of similar roles. Most pay
$180,000–$281,300
— the blue band above.
At the midpoint, this role pays about
$300k
versus about
$231k
for comparable roles.
Based on 240 similar postings.
Employer
About Amd
AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors
Amd currently has
496 open roles
on FindRole.
Listed pay typically runs
$172,000–$258,000
across 496 roles with salary data.
The Head of AI Engineering Productivity, Global Cluster Engineering is an individual contributor role focused on leading AI enablement and accelerating the adoption of agentic systems across software and hardware engineering workflows. The successful candidate will define and execute a roadmap to operationalize cutting-edge AI, building scalable platforms, APIs, frameworks, and services to improve developer velocity and hardware validation cycles. Key responsibilities include deploying autonomous agents for issue triage, developing reusable agent frameworks for tool orchestration and memory management, and establishing best practices for model integration and governance. Required expertise includes Large Language Models, agentic architectures, AI-native developer workflows, and multimodal models. This role solves the technical challenge of replacing manual processes with intelligent, AI-driven workflows to enhance organizational effectiveness and inference efficiency at scale.
Define and execute the AI enablement strategy across software engineering, hardware development, and business functions.
Lead the adoption of AI copilots, autonomous agents, and intelligent workflows throughout the engineering lifecycle.
Deploy agentic systems to autonomously triage issues, route work, and propose solutions in large-scale environments.
Build scalable AI platforms, APIs, frameworks, and services to accelerate safe AI adoption across teams.
Develop reusable agent frameworks for tool orchestration, memory management, and workflow automation.
Identify and replace manual processes with intelligent, AI-driven workflows to improve organizational efficiency.
Optimize AI platform performance, inference efficiency, and operational costs at an enterprise scale.
Evaluate emerging AI ecosystems and multimodal models to guide strategic technology investments.
What we're looking for
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, AI, Machine Learning, Software Engineering, or a related technical field.
Master’s or PhD degree (preferred).
Deep expertise in AI/ML systems, LLMs, agentic architectures, modern AI tooling, and AI-native developer workflows (preferred).
Hands-on experience with agent frameworks, tool use, orchestration systems, memory architectures, and model deployment (preferred).
Experience delivering platform-level products and production-ready AI solutions within large-scale engineering organizations (preferred).
Expertise implementing intelligent automation solutions like automated bug triage, workflow automation, and conversational AI (preferred).
Experience optimizing inference performance, AI infrastructure, scalability, and deployment costs at enterprise scale (preferred).
Proven track record delivering complex technology platforms and driving cross-organizational transformation initiatives (preferred).
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Agentic AI
Machine Learning
ML Ops
Large Language Models
LLMs
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Computer Vision
Agile
Data Science
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Data Governance
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Agentic AI
Natural Language Processing
Computer Vision
Machine Learning
Cloud-native Architecture
Data Pipelines
AI SDLC
HIPAA
Intelligent Automation
Document Intelligence
Artificial Intelligence
Machine Learning
Python
Java
C++
TensorFlow
Scikit-Learn
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Data Engineering
Data Wrangling