GenAI Software Development Engineer

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.

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.

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 LOCATION: Santa Clara Austin or Seattle or Secaucus This role is not eligible for Visa transfer or Sponsorship #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.