• Define the long-term technical vision and architecture for AI-powered developer tools and the self-service platform that enables teams to build their own AI agents • Establish architectural patterns, technical standards, and best practices for LLM integration, AI agent development, and production AI systems serving developers • Lead technical strategy for platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools • Design highly available, scalable infrastructure for hosting AI agents and developer tools with predictable performance and intelligent resource management • Drive technical decisions on AI technology choices, LLM provider strategies, prompt engineering approaches, and agent orchestration frameworks • Partner with Senior Manager and product leadership to align technical architecture with business objectives and productization opportunities • Architect and build production-ready AI agents for developer productivity including code review assistants, test generators, deployment diagnostics, and incident response automation • Design and implement the self-service platform infrastructure that reduces time-to-production for new AI tools from weeks to days • Build systems that accelerate adoption of AI-native development tools (Claude Code, Cursor, Warp) across Docker's engineering organization • Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks to inform Docker's technical strategy • Drive technical standards for measuring AI tool effectiveness including adoption metrics, productivity gains, and developer satisfaction.