• Full ownership of their GCP/Vertex AI infrastructure, APIs, and frontend • Architecture decisions that will scale from dozens to thousands of users • Production reliability, deployment pipelines, and technical debt management • Security, compliance, and data integrity across all systems • Define deliverables for specialist contractors (frontend, DevOps, ML) • Review PRs from both humans and AI-generated code • Build and maintain internal agentic workflows—treating AI agents as asynchronous team members • Document patterns so contractors and agents can work autonomously against clear specs • Pioneer their "1 dev × N agents" operating model • Build reusable prompts, agent configs, and automation scaffolding • Establish workflows where AI handles first drafts, boilerplate, tests, and docs—you handle architecture, edge cases, and judgment calls • Continuously evaluate new AI dev tools and integrate what works • Audit and document the current stack; identify tech debt and quick wins • Establish CI/CD and deployment workflows optimized for rapid iteration • Ship at least one customer-facing feature end-to-end • Build the contractor onboarding playbook (specs, PR standards, async communication) • Implement at least one agentic workflow that measurably accelerates development