Manager Software Development

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 AMDis shapingthefuture.

Whether you’redesigning 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.

We are building a platform where autonomous AI agents run hardware validation campaigns, triage failures, and continuously grow a shared knowledge base — without a human in the loop. You will be a core engineer on this system, designing and building the LLM agent framework, RAG pipelines, MCP backend, and developer tooling that make it work. This role sits within the Global Cluster Engineering organization, where you will develop software that powers distributed infrastructure at global scale.

This is an AI-native software engineering role: you will spend your time building multi-agent orchestration systems, retrieval-augmented generation pipelines, tool-use frameworks, and knowledge graph integrations. You do not need deep hardware domain knowledge — but intellectual curiosity about how firmware validation and network hardware works will help you build better tools for the engineers who do. We are hiring two Senior Software Engineers into this role;

specific areas of ownership will be shaped by each person's strengths and interests.

• Experience: Software development experience, with a strong portfolio of production systems
• Engineering Leadership: Experience leading and growing engineering teams — hiring, mentoring, and developing engineers, setting technical direction, running delivery for multi-workstream projects.
• AI-Native Development: Genuine passion for building AI-native software — you follow the field, have shipped real LLM-powered systems, and care about getting the details right (grounding, evaluation, failure modes, not just prompts)
• RAG Systems: Hands-on experience building RAG pipelines — embedding models, vector databases, chunking strategies, retrieval evaluation, hybrid search, and reranking
• LLM Engineering: Production experience with LLM tool use, multi-agent orchestration, prompt engineering, context management, and hallucination mitigation
• Core
  
  Skills:
  
  Strong proficiency in one or more modern programming languages such as Python, Type Script/Node.js, Go, Java, C#, or Rust, with demonstrated ability to build and operate production-scale services. Python experience is preferred due to the AI/ML ecosystem
• Engineering excellence: Async programming, API design, distributed systems, clean code practices. Experience designing for reliability in automated/unattended environments — crash recovery, audit trails, state management, observability
• Cloud Infrastructure: Experience with AWS, Azure, or GCP — infrastructure provisioning, managed services, networking, and deploying production workloads at scale
• AI Tooling: Active use of AI coding assistants and LLM-powered developer tools (Claude Code, Git Hub Copilot, Cursor, etc.) to accelerate development and problem-solving

• Agent Orchestration: Design, build, and maintain the AI agent orchestration layer — multi-agent dispatch, context window management, anti-hallucination guardrails, progress tracking, crash recovery, audit trails, and inter-agent communication protocols
• RAG Pipeline Development: Build and continuously improve the retrieval-augmented generation pipeline — document ingestion from Slack, Git Hub, Jira, and Confluence; chunking and embedding strategies; hybrid…