Lead Software Engineer – Java/back-end With AI

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

Lead Software Engineer focused on integrating AI-assisted engineering practices into the software development lifecycle for a financial institution. The role involves driving team adoption of AI tools for code quality, delivery speed, and operational improvements, while ensuring responsible and compliant use.

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. Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

Skills

Required

  • Java fundamentals
  • object-oriented programming
  • multi-threading
  • messaging technologies
  • computer networks
  • high-performance, latency-sensitive Java applications
  • AI-assisted software development tools
  • responsible AI use
  • cloud
  • artificial intelligence
  • machine learning
  • mobile technologies
  • distributed systems
  • microservices
  • event-driven architectures
  • Linux environments
  • containers
  • cloud architectures/services
  • interpersonal and communication skills
  • global teams
  • continuous learning and improvement

Nice to have

  • modern front-end technologies
  • React
  • Angular
  • Vue.js
  • AWS
  • Azure
  • GCP
  • Python
  • front-end development
  • FIX messaging protocol
  • QuickFIX/J
  • Hazelcast
  • distributed computing platforms

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