Lead Software Engineer - Java, Aws

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Consumer & Community Banking

Lead Software Engineer with Java and AWS experience in a financial services company. The role focuses on developing and enhancing technology products, with a significant emphasis on driving team adoption of AI-assisted engineering practices for code quality, delivery speed, and operational outcomes. Responsibilities include executing software solutions, developing secure code, identifying automation opportunities, leading evaluation sessions, and contributing to team culture. Requires proven expertise in system design, application development, testing, Java, REST APIs, SQL/NoSQL, agile methodologies, and responsible AI use in 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 break down technical problems
  2. Develops secure high-quality production code, and reviews and debugs code written by others
  3. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  4. 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.
  5. 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
  • Proven expertise in system design, application development, testing, and ensuring operational stability.
  • Advanced in one or more programming language(s): Java
  • Experience developing and supporting REST API interfaces (SpringBoot, Swagger), as well as working with SQL and NoSQL technologies is required.
  • Proficiency in automation and continuous delivery methods
  • Proficient in all aspects of the Software Development Life Cycle
  • Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
  • 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
  • Experience architecture, analysis, design, coding, and code review and application, data, and infrastructure architecture disciplines.
  • Proficiency with a variety of software engineering toolsets and experience with tracking record of success working on highly distributed systems.

Nice to have

  • Knowledge in AWS, Cassandra
  • Proficiency in other modern programming languages in addition to Java: e.g. Python, Groovy

What the JD emphasized

  • Proven expertise in system design, application development, testing, and ensuring operational stability.
  • Experience developing and supporting REST API interfaces (SpringBoot, Swagger), as well as working with SQL and NoSQL technologies is required.
  • 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