AI Infrastructure and Engineering Automation Lead

Key Responsibilities

• Research, evaluate, prototype, and deploy AI-driven engineering and manufacturing tools across the organization.
• Identify opportunities where AI can improve:
  • engineering productivity
  • product development speed
  • architecture development
  • design validation
  • manufacturing quality
  • customer response workflows
  • operational efficiency
• Develop AI-assisted workflows for infrastructure and hardware engineering including:
  • automated specification analysis
  • design rule extraction and checking
  • board and platform design automation
  • power and thermal analysis assistance
  • architecture document generation
  • validation automation
  • configuration analysis
• Explore AI solutions for:
  • manufacturing inspection
  • defect detection
  • quality analysis
  • telemetry analytics
  • failure prediction
  • operational optimization
• Evaluate emerging AI tooling ecosystems, models, and infrastructure platforms relevant to engineering and manufacturing environments.
• Build proof-of-concepts and pilot programs demonstrating measurable engineering or operational efficiency gains.
• Collaborate across architecture, engineering, manufacturing, operations, and IT organizations to integrate AI-enabled workflows.
• Help define long-term AI tooling and automation strategy for the CTO organization.
• Support internal AI tooling infrastructure including data pipelines, workflow orchestration, and engineering integrations where applicable.

Qualifications

• 5–10+ years in AI/ML, applied research, software engineering, infrastructure automation, or related technical fields.
• Strong understanding of modern AI and LLM technologies and practical application of AI workflows.
• Experience developing automation tools, AI pipelines, or engineering productivity solutions.
• Familiarity with one or more of: infrastructure engineering, hardware development, manufacturing systems, validation workflows, telemetry systems, operations analytics.
• Strong software development and scripting skills.
• Experience with AI frameworks, APIs, orchestration tools, or model integration workflows.
• Strong problem-solving and systems-thinking capability.
• Able to work cross-functionally with engineering and architecture teams.

Preferred Experience

• Experience applying AI to hardware engineering or manufacturing environments.
• Familiarity with PCB/platform development workflows.
• Experience with computer vision or manufacturing inspection systems.
• Understanding of datacenter infrastructure, AI systems, or server platform development.
• Experience working with large technical document sets, specifications, or structured engineering data.
• Familiarity with retrieval-augmented generation (RAG), agentic workflows, and engineering knowledge systems.