AI Workflow Engineer (Silicon Engineering Productivity)

Job Description:
We are seeking a contract engineer to accelerate development of AI-powered engineering productivity solutions for silicon IP and SoC development. The successful candidate will work directly with engineering leadership to design, implement, and deploy AI skills, agents, workflow automations, and knowledge systems that improve engineering efficiency across design, verification, integration, and project execution.
This is an applied engineering role focused on building practical solutions rather than conducting AI research.

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:

• ospecification analysis
• otest-plan review
• ocoverage analysis
• ocode review
• odebug assistance
• oproject tracking
• oengineering reporting
• Build 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.

Required Qualifications

• 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.

Strongly Preferred

• Semiconductor industry experience.

Experience integrating AI solutions with:

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

What Success Looks Like (First 6 Months)
The contractor delivers several reusable AI assets that are actively used by engineering teams, including examples such as:

• Engineering knowledge assistants.
• Test-plan analysis and review agents.
• Verification productivity tools.
• Debug and failure-triage assistants.
• Reporting and operational automation solutions.
• Workflow orchestration agents spanning multiple engineering tools.
• Success is measured by adoption, engineer time savings, workflow completion rates, and reduction of repetitive manual work.