Lead Software Engineer - Java With Agentic AI

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

Lead Software Engineer role focused on integrating and driving adoption of AI-assisted engineering practices within an existing enterprise software development lifecycle, primarily using Java. The role emphasizes using AI tools to improve code quality, delivery speed, and operational outcomes, with a strong focus on responsible AI use, validation of AI outputs, and coaching other engineers.

What you'd actually do

  1. Drives 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.
  2. 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.
  3. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  4. Develops secure high-quality production code, and reviews and debugs code written by others
  5. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

Skills

Required

  • Java
  • J2EE
  • Spring Boot
  • AI coding assistants/agents
  • test scenarios
  • automating unit and integration tests
  • performance testing
  • code optimization
  • MQ
  • Kafka
  • message parsers
  • Oracle
  • Cassandra
  • Bitbucket
  • Git
  • Jules
  • CLOUD

Nice to have

  • ReactJS
  • Oracle JDK17
  • Oracle JDK21

What the JD emphasized

  • 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