AI Engineer (Software Development Engineer)

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.

Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.

THE ROLE:

We are seeking a passionate and highly motivated AI Engineer to join our AI Engineering team focused on building the next generation of Agentic AI and Digital Workers.

In this role, you will participate in the complete lifecycle of AI solution development, from prompt engineering and context engineering to deploying AI agents into real business environments. You will work alongside senior AI engineers, business stakeholders, and domain experts to build intelligent systems that automate workflows, enhance productivity, and transform enterprise operations.

This is an excellent opportunity for early-career engineers who are excited about Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), and enterprise AI deployment.

THE PERSON:

WHAT YOU WILL GAIN

• Hands-on experience building real-world Agentic AI systems

• Exposure to enterprise-scale AI deployment and Digital Worker platforms

• Mentorship from experienced AI engineers and architects

• Opportunities to work with cutting-edge LLM technologies and AI frameworks

• Direct involvement in AI transformation initiatives across multiple business functions

• Career growth path toward AI Engineer, Senior AI Engineer, AI Architect, or Forward Deployment Engineer roles

KEY RESPONSIBILITIES:Agentic AI Development

• Design, develop, and test AI agents capable of multi-step reasoning and workflow execution

• Build solutions leveraging LLMs, tool usage, memory, and agent orchestration frameworks

• Implement function calling, structured outputs, and automated workflows

• Develop Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources

• Optimize AI system performance, reliability, scalability, and response quality

• Assist in evaluating and benchmarking AI model effectiveness

Prompt & Context Engineering

• Design effective system prompts and task-specific prompts