AI Workflow Engineer (Silicon Engineering Productivity)

Responsibilities

• Develop AI agents and skills that automate multi-step engineering workflows.
• Build retrieval-augmented generation (RAG) systems for engineering knowledge, specifications, design documentation, test plans, and project data.
• Create workflow automations that connect engineering tools, repositories, documentation systems, and reporting environments.
• Design and implement copilots, assistants, and agentic workflows for:
  • specification analysis
  • test-plan review
  • coverage analysis
  • code review
  • debug assistance
  • project tracking
  • engineering reporting
• Develop proof-of-concept solutions and mature them into reusable assets.
• Evaluate emerging AI models, frameworks, orchestration techniques, and agent architectures.
• Develop metrics and dashboards demonstrating engineering productivity improvements.
• Work closely with design, DV, program management, and CAD teams to identify high-value automation opportunities.

Requirements

• BS/MS in Computer Engineering, Computer Science, Electrical Engineering, or equivalent experience.
• Understanding of RTL design, DV, simulation, regression, coverage, or SoC development workflows.
• Demonstrated experience building AI applications beyond prompt engineering.
• Experience creating at least one production-quality:
  • AI agent
  • AI skill/plugin
  • workflow automation
  • MCP-based solution
  • agent orchestration system
• Strong Python development experience.
• Experience with APIs, automation frameworks, and software integration.
• Working knowledge of:
  • LLMs
  • RAG architectures
  • vector databases
  • agent frameworks
  • prompt/context engineering
• Ability to independently decompose ambiguous problems and deliver working solutions.

Preferred

• Semiconductor industry experience.
• Experience integrating AI solutions with:
  • GitHub
  • Perforce
  • Confluence
  • SharePoint
  • Jira
  • Teams
  • engineering databases
• Experience building AI solutions that operate on proprietary engineering documentation and source repositories.
• Familiarity with secure enterprise AI deployments.