AI Agent Engineer

About AMD

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: AI Agent Engineer

As an AI Agent Engineer at AMD, you will be instrumental in developing and optimizing advanced AI solutions. This role focuses on building robust multi-agent workflows, enhancing training reliability, and improving distributed training performance to maximize GPU utilization.

Responsibilities

• Build and integrate multi-agent workflows.
• Expose platform capabilities via standard APIs and agent protocols (e.g., MCP/A2A).
• Improve training reliability through automation, failover, and health checks to ensure continuous job execution during cluster faults.
• Optimize distributed training performance, including parallelism, communication, storage, and operator tuning, to enhance GPU utilization.
• Own observability and debugging processes, utilizing logs, metrics, traces, profiling, and visualization techniques.

Requirements

• Strong background in software and systems engineering, with experience in large-scale/distributed training.
• Familiarity with a multi-agent framework (e.g., CrewAI, LangGraph) or a training stack (e.g., Megatron/DeepSpeed).
• Proven performance profiling and bottleneck isolation skills.
• Awareness of protocol and security aspects for agent connectivity and governance.

Nice to Have

• SFT/RL fine-tuning experience.
• End-to-end agent + LLM training pipeline experience.
• TorchFT (or similar) fault-tolerant training experience.

Academic Credentials

Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.

Key skills/competency

• AI Agent Development
• Multi-agent Systems
• Distributed Training
• GPU Optimization
• Software Engineering
• Systems Engineering
• Performance Profiling
• API Integration
• Observability
• LLM Pipelines