AI Engineering Lead—Technical Infrastructure bei Opus Inspection
Opus Inspection · Tucson, Vereinigte Staaten Von Amerika · Onsite
- Senior
- Optionales Büro in Tucson
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