Lead Software Engineer - Java

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

Lead Software Engineer at JPMorgan Chase focused on enhancing, building, and delivering technology products within the Consumer and Community Banking's technology team. The role involves executing software solutions, developing secure code, and driving team adoption of enterprise-authorized AI-assisted engineering practices. Responsibilities include troubleshooting, leading evaluation sessions, and ensuring responsible AI use in engineering workflows. Requires strong Java and AWS experience, API development at scale, and understanding of SDLC and agile methodologies.

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

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • Advanced in one or more programming language(s): Java
  • Hands on development experience in building and supporting complex java (Spring) applications
  • Experience deploying applications in AWS infra with EAC and terraform.
  • Ability to troubleshoot and fix issues on deployed applications using Grafana , Splunk etc.
  • Experience with building APIs at scale that can handle very high volume ( High TPS).
  • 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 in all aspects of the Software Development Life Cycle
  • Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

Nice to have

  • In-depth knowledge of the financial services industry and their IT systems
  • Practical cloud native experience
  • Experience with dealing with critical applications that has less than 2 hrs with time for resolution.
  • Knowledge of AI models and ability to develop apps using models ( Claude, GPT etc.).

What the JD emphasized

  • Advanced in one or more programming language(s): Java
  • Hands on development experience in building and supporting complex java (Spring) applications
  • Experience deploying applications in AWS infra with EAC and terraform.
  • Ability to troubleshoot and fix issues on deployed applications using Grafana , Splunk etc.
  • Experience with building APIs at scale that can handle very high volume ( High TPS).
  • 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