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AI Engineering Lead—Technical Infrastructure bei Opus Inspection

Opus Inspection · Tucson, Vereinigte Staaten Von Amerika · Onsite

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The AI Engineering Lead—Technical Infrastructure will drive our organization's, transformation into an AI-augmented engineering powerhouse. This role will shape how our 40+ engineers leverage AI to modernize legacy systems, accelerate development, and deliver breakthrough innovations.

 

Duties & Responsibilities

Project Scaffolding & Acceleration

 

  • Execute sprint-based rapid interventions: In 1-2 week sprints, transform critical but neglected codebases (e.g., convert a 10,000-line undocumented VB6 module into documented, tested, AI-ready C# with comprehensive handoff materials)
  • Deploy for rapid engagements where product management identifies high-impact opportunities
  • Create hand-off packages that enable seamless transitions to responsible teams, including architecture diagrams, test suites, and AI-ready documentation
  • Serve as an "AI pair programmer" trainer for critical modernization initiatives
  • Transform undocumented legacy code into maintainable, AI-ready codebases with 90%+ test coverage

Innovation & Strategic Development

 

  • Identify opportunities for ML/AI enhancement across products and processes
  • Evaluate and prototype AI-powered features such as:
    • Fraud detection and automated validation systems
    • Intelligent reporting and analytics dashboards
    • Automating compliance reporting with NLP-based document analysis
  • Own company-wide AI models, platforms, and tools inventory
  • Develop AI capabilities for customer engagement, analytics, and operational excellence
  • Stay current on emerging AI technologies and translate them into practical use cases
  • Partner with leadership to define long-term AI strategy and roadmap

Technical Infrastructure

  • Design and implement centralized AI documentation pipelines
  • Build automated code generation and review systems
  • Create secure AI model integration frameworks
  • Optimize AI infrastructure costs and performance
  • Develop reusable components and starter kits
  • Partner with DevOps to create reliable AI-enhanced CI/CD pipelines

 

Team Enablement & Culture Building

 

  • Create role-specific training materials for different engineering disciplines
  • Build and maintain a library of prompts, templates, and best practices
  • Establish and coordinate an AI Champions network across all teams
  • Own and expand AI Office Hours program with participation and adoption metrics
  • Convert AI skeptics through 1-on-1 sessions showing personalized productivity gains
  • Create "safe failure" environments where engineers can experiment without judgment
  • Document and address common concerns (job security, code quality, learning curve)
  • Design engagement initiatives including challenges, contests, and gamified learning platforms
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