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Manager Software Development

AMD· Engineering
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Santa Clara, California$195.6kFull-timePosted 5d ago

About the role

Senior software engineering leadership role focused on building an AI-native platform for autonomous AI agents to run hardware validation campaigns, with responsibilities spanning agent orchestration, RAG pipelines, backend systems, developer tooling, and end-to-end ownership in a distributed infrastructure at global scale.

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
  • 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
  • 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
  • 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

Qualifications

  • BS or MS Degree in Computer Science, Electrical Engineering, or related field
  • 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)
  • 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: Proficiency in Python, TypeScript/Node.js, Go, Java, C#, or Rust; Python preferred
  • Engineering excellence: Async programming, API design, distributed systems, clean code practices; reliability in automated/unattended environments
  • Cloud Infrastructure: Experience with AWS, Azure, or GCP — provisioning, managed services, networking, deploying production workloads
  • AI Tooling: Active use of AI coding assistants and LLM-powered developer tools (e.g., Claude Code, GitHub Copilot)

Skills

Required:Software developmentLeadership / people managementRAG pipelinesLLM tooling & orchestration

Benefits

  • Benefits described: AMD benefits at a glance
Last updated Oct 22, 2026. Interested? Apply on the company site.
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