Sr. Lead Software Engineer

JPMorgan Chase JPMorgan Chase · Banking · Houston, TX +1 · Commercial & Investment Bank

This role focuses on driving the adoption and governance of AI-assisted engineering practices within the Software Development Life Cycle (SDLC) to improve code quality, delivery speed, and operational outcomes. The engineer will leverage AI tools for tasks like code review, test acceleration, and incident analysis, while ensuring responsible AI usage, data sensitivity, and security compliance. The role requires experience in system design, application development, and leading the effective use of enterprise-authorized AI tools.

What you'd actually do

  1. Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
  2. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
  3. Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
  4. Develops secure and high-quality production code, and reviews and debugs code written by others
  5. Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • Advanced in one or more programming language(s)
  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.

Nice to have

  • AI-assisted code review/refactoring
  • test acceleration
  • release readiness
  • incident/root-cause analysis
  • secure coding
  • peer review
  • automated testing
  • reuse of proven patterns and automation

What the JD emphasized

  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.