AI Engineering Lead-Technical Infrastructure

OPUS Global•Tucson, AZ

44d

About The Position

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.

Requirements

3+ years of software development in C#/.NET or similar enterprise environments
Active daily user of AI development tools with demonstrated productivity gains
Strong communication skills to influence technical and non-technical stakeholders
Experience with Git/Azure DevOps and modern development practices
Proven ability to learn quickly and adapt to emerging technologies

Nice To Haves

5+ years of development experience with complex system architecture preferred
Deep expertise in REST APIs, service integration, and DevOps automation preferred
Experience with AI/ML model deployment and optimization preferred
Strong database and data pipeline skills preferred

Responsibilities

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 DevOps 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