Java Sr Lead Software Engineer - Oms/sales - VP

JPMorgan Chase JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Commercial & Investment Bank

Senior Lead Software Engineer for OMS/Sales at JPMorgan Chase in London, focusing on enhancing, building, and delivering technology products. The role involves providing technical guidance, developing secure code, and driving the adoption of AI-assisted engineering practices to improve code quality and delivery speed. Requires strong Java backend skills, cloud platform experience (AWS, Terraform), SQL/NoSQL database knowledge, and experience with enterprise-authorized AI development tools, including coaching on compliant usage and responsible AI principles.

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. Drives decisions that influence the product design, application functionality, and technical operations and processes

Skills

Required

  • Java
  • Springboot
  • High performance java
  • thread safety context
  • lockless approach
  • controlled object generation practices
  • AWS
  • Terraform
  • SQL
  • NoSQL databases
  • enterprise-authorized AI-assisted software development tools
  • responsible AI use in engineering workflows
  • data sensitivity considerations
  • secure handling of inputs/outputs
  • resiliency and security expectations
  • coaching senior engineers/leads on compliant usage patterns and controls
  • cloud native experience

Nice to have

  • AI-assisted code review/refactoring
  • test acceleration
  • release readiness
  • incident/root-cause analysis
  • secure coding
  • peer review
  • automated testing

What the JD emphasized

  • AI-assisted engineering practices
  • enterprise-authorized AI-assisted software development tools
  • responsible AI use in engineering workflows