Lead Software Engineer - Backend Engineer - Java and AI

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Consumer & Community Banking

Lead Software Engineer role focused on building data-centric, low-latency, high-throughput distributed applications and APIs. The role emphasizes driving team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while ensuring secure coding, testing, and responsible AI use within engineering workflows.

What you'd actually do

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

Skills

Required

  • Java
  • Spring Boot
  • AWS cloud technologies
  • Kafka
  • Software Development Life Cycle
  • agile methodologies
  • CI/CD
  • Application Resiliency
  • Security
  • formal training or certification on software engineering concepts
  • 5+ years applied experience
  • system design
  • application development
  • testing
  • operational stability
  • leading effective use of approved AI-assisted software development tools
  • setting team expectations for validating AI outputs for correctness, performance, and security
  • strong understanding of responsible AI use in engineering workflows
  • data sensitivity considerations
  • secure handling of inputs/outputs
  • adherence to resiliency and security expectations

Nice to have

  • In-depth knowledge of the financial services industry and their IT systems
  • Practical cloud native experience

What the JD emphasized

  • enterprise-authorized AI-assisted engineering practices
  • AI-assisted code review/refactoring
  • AI-assisted development
  • responsible AI use in engineering workflows
  • coaching engineers on safe, compliant adoption