Job Description

• Shape the end-to-end AWS cloud architecture strategy across all product components.
• Advance the resiliency strategy — multi-region, active-active, failover/failback, and DR.
• Strengthen cloud cost strategy and optimization across the product.
• Evolve the automation and infrastructure-as-code strategy.
• Mature AI integration across engineering and product workflows.
• Raise the bar on shared architectural standards and decision-making across teams.
• Sharpen how complex technical strategy is distilled into leadership-facing narratives.
• Promotes team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
  
  

Required qualifications, capabilities, and skills

• Deep, broad AWS expertise across compute, data, networking, and security, with proven production architecture work.
• Systems thinking — reasoning about trade-offs, failure modes, and dependencies across an entire product.
• Strategic command of cost, scalability, automation, and AI integration as connected levers.
• Hands-on depth to validate and prototype ideas, not just advise.
• Executive communication and rigorous, independent judgment in a regulated, high-stakes environment.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices