• Embed directly within product engineering teams to drive measurable improvements in developer productivity by integrating AI into daily workflow design, coding, testing, deployment, and operations • Lead tool selection and adoption strategy for AI development platforms (GitHub Copilot, Claude Code, Cursor, etc.), establishing best practices for prompt engineering, context management, and workflow integration • Build and maintain custom AI tooling, MCP servers, and integrations that provide teams with domain-specific context from your codebase, documentation, infrastructure, and business systems • Develop and socialize reusable patterns for code generation, refactoring, test automation, incident analysis, and knowledge retrieval that teams can apply across their daily work • Implement AI code review and validation practices to ensure AI-generated code meets security, quality, and HIPAA compliance standards • Champion a culture of AI-augmented development that enables engineers to tackle bigger challenges, improve code quality, and reduce toil—without sacrificing maintainability or creating technical debt • Create self-service documentation, learning paths, and enablement programs (workshops, office hours, communities of practice) to scale AI adoption across engineering • Measure and communicate impact using both quantitative metrics (velocity, quality, time-to-deployment) and qualitative measures (developer satisfaction, cognitive load reduction, friction logging) • Lead application-level infrastructure-as-code initiatives, empowering product teams to own their cloud resources (containers, SQS, Lambda, DocumentDB, DynamoDB, etc.) • Design reference architectures and terraform/CDK patterns that balance team autonomy with consistency and security • Partner with the enterprise DevOps team to establish clear boundaries—they own foundational infrastructure (VPCs, security groups, IAM foundations), you enable teams to own application-layer resources • Build proof-of-concepts and migration playbooks as GHX transitions from lift-and-shift EC2 environments to truly cloud-native architectures • Manage engineers on cloud-native design patterns, observability, cost optimization, and operational excellence • Provide direction, hands-on execution to grow an engineering team • Work closely with product managers, engineering leaders, and cross-functional partners to translate business needs into practical AI and infrastructure solutions • Establish metrics and feedback loops to continuously improve both AI adoption and cloud-native maturity • Balance innovation with pragmatism—championing new approaches while ensuring solutions scale sustainably.