Lead Software Engineer

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Corporate Sector

Lead Software Engineer for the Corporate Technology, Risk Technology team, focusing on site reliability. The role involves leading initiatives to improve application reliability and stability, driving team adoption of enterprise-authorized AI-assisted engineering practices for code quality, delivery speed, and operational outcomes, and ensuring responsible AI use with a focus on data sensitivity, security, and compliance. The engineer will also serve as a point of contact during major incidents and mentor 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

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Demonstrated proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices
  • Fluent in at least one programming language such as: Python, Java/Spring Boot, .Net
  • 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
  • Proficient knowledge and experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection
  • Proficient with continuous integration and continuous delivery practices and tooling
  • Proficient with container and container orchestration
  • Experience with troubleshooting common networking technologies and issues
  • Advanced knowledge of software applications and technical processes with emerging depth in one or more technical disciplines, and actively self-educates to evaluate and recommend suitable new technologies

Nice to have

  • Ability to proactively recognize road blocks and demonstrates interest in learning technology that facilitates innovation
  • Ability to identify new technologies and relevant solutions to ensure design constraints are met by the software team
  • Ability to initiate and implement ideas to solve business problems

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