AI Engineering Lead—Technical Infrastructure

AI Engineering Lead—Technical Infrastructure

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.

• 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

• 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

• 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 Dev Ops to create reliable AI-enhanced CI/CD pipelines

• 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