Job description

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 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. THE PERSON: • 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, TypeScript/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, GitHub Copilot, Cursor, etc.) to accelerate development and problem-solving KEY RESPONSIBILITIES: • 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, GitHub, Jira, and Confluence; chunking and embedding strategies; hybrid vector + keyword search; cross-encoder and LLM-based reranking; knowledge graph indexing via LightRAG + Neo4j • Developer Experience & User Interfaces : Build intuitive web applications and developer experiences enabling engineers to interact with AI agents, knowledge systems, validation workflows, observability dashboards, and operational tooling. Experience building modern web applications using React, Next.js, Angular, Vue, or similar frameworks. • Backend Systems: Design and implement distributed services, APIs, event-driven architectures, and microservices powering AI workflows and platform integrations. • AI Services: Design and implement scalable, low-latency AI services powering metadata generation, feature extraction, and knowledge retrieval — ensuring agents have accurate, grounded context at query time • LLM-Powered Tooling: Build LLM-powered developer tooling — automated test plan generation, test case quality auditing, AI-driven failure triage, autonomous knowledge curation after every test run, and intelligent report generation • Agentic AI Deployment: Develop and deploy agentic AI solutions — autonomous agents, multi-agent orchestration frameworks, and LLM-powered workflows — that transform validation operations across hardware teams • Stakeholder Collaboration: Work closely with validation engineers, hardware teams, and engineering peers to translate business and domain requirements into flexible, well-designed software solutions • Security & Compliance: Ensure AI/ML systems comply with security standards and best practices, addressing data privacy and protection concerns across all LLM integrations, RAG pipelines, and credential-handling systems • End-to-End Ownership: Own features end-to-end — from project estimation and architecture review through coding, deployment, and post-launch measurement • Operational Excellence: Build resilient systems with strong observability; implement automated testing, monitoring, and CI/CD pipelines using infrastructure-as-code tools (Terraform); participate in on-call rotations and drive root-cause analysis and reliability improvements • Code Quality: Write clean, well-tested, maintainable code — other engineers and agents depend on what you build; quality directly affects system reliability • Onboarding: Collaborate with validation engineers to translate domain knowledge into agent skills and onboard new product teams onto the platform PREFERRED EXPERIENCE: • Design and implement distributed services, APIs, event-driven architectures, and microservices powering AI workflows and platform integrations. • Experience with the Model Context Protocol (MCP) or agentic platforms (Claude Code, LangGraph, CrewAI, AutoGen) • Familiarity with knowledge graph systems (Neo4j, LightRAG) or graph-augmented RAG • Background in the semiconductor, datacenter, or networking industry — high-level understanding of how hardware validation or firmware development works • Experience with CI/CD systems and automated test infrastructure • Exposure to Slack API, GitHub API, or Atlassian REST APIs • Data Engineering & Analytics: Experience with data pipeline design, ETL workflows, data warehousing, or analytics platforms is a plus ACADEMIC CREDENTIALS: BS or MS Degree in Computer Science, Electrical Engineering, or related field This role is not eligible for Visa Sponsorship or Transfer #LI-KW1 Benefits offered are described: AMD benefits at a glance . AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Requirements

Must-haves

ADVANCE YOUR CAREER. ADVANCE THE WORLD.
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.
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
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, GitHub Copilot, Cursor, etc.) to accelerate development and problem-solving
Backend Systems: Design and implement distributed services, APIs, event-driven architectures, and microservices powering AI workflows and platform integrations.
AI Services: Design and implement scalable, low-latency AI services powering metadata generation, feature extraction, and knowledge retrieval — ensuring agents have accurate, grounded context at query time
Agentic AI Deployment: Develop and deploy agentic AI solutions — autonomous agents, multi-agent orchestration frameworks, and LLM-powered workflows — that transform validation operations across hardware teams
Stakeholder Collaboration: Work closely with validation engineers, hardware teams, and engineering peers to translate business and domain requirements into flexible, well-designed software solutions
Security & Compliance: Ensure AI/ML systems comply with security standards and best practices, addressing data privacy and protection concerns across all LLM integrations, RAG pipelines, and credential-handling systems
End-to-End Ownership: Own features end-to-end — from project estimation and architecture review through coding, deployment, and post-launch measurement
Code Quality: Write clean, well-tested, maintainable code — other engineers and agents depend on what you build; quality directly affects system reliability
Onboarding: Collaborate with validation engineers to translate domain knowledge into agent skills and onboard new product teams onto the platform
BS or MS Degree in Computer Science, Electrical Engineering, or related field
Benefits offered are described: AMD benefits at a glance .

Nice-to-haves

Design and implement distributed services, APIs, event-driven architectures, and microservices powering AI workflows and platform integrations.
Experience with the Model Context Protocol (MCP) or agentic platforms (Claude Code, LangGraph, CrewAI, AutoGen)
Familiarity with knowledge graph systems (Neo4j, LightRAG) or graph-augmented RAG
Background in the semiconductor, datacenter, or networking industry — high-level understanding of how hardware validation or firmware development works
Experience with CI/CD systems and automated test infrastructure
Exposure to Slack API, GitHub API, or Atlassian REST APIs
Data Engineering & Analytics: Experience with data pipeline design, ETL workflows, data warehousing, or analytics platforms is a plus

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aillmpythontypescriptnode.jsgojavarustmlapiawsazuregcpjirareactnext.jsangularvueci/cdrest

Platform: amd
Effort: medium

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