Lead Software Engineer -Cloud Architecture & AI

Job Description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Consumer and community banking technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job Responsibilities

• 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
  
  

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