Opus
AI Engineering LeadTechnical Infrastructure
The AI Engineering LeadTechnical 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 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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The AI Engineering LeadTechnical 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 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
#J-18808-Ljbffr