Lead Software Engineer - Java / Equities Risk

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

Lead Software Engineer for Equities Risk Management team at JPMorgan Chase, focusing on building and enhancing a real-time derivatives pricing and risk management platform. The role involves driving team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed, while ensuring responsible AI use and adherence to security and compliance standards.

What you'd actually do

  1. Works directly with business, quant and technology teams to articulate new technology requirements and solve business problems
  2. Collaborates with other members of a globally distributed team to brainstorm new ideas / solutions, and provide mentoring and technical expertise to the team
  3. Develops specific enhancements and/or build new solutions to fulfill business objectives related to new business requirements
  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
  • Advanced hands-on coding experience with Java technologies/frameworks such as Spring/Spring Boot, Spring JPA/Hibernate, and REST based services
  • Strong experience with JVM performance analysis, including profiling, heap dump analysis, thread dump analysis, and GC tuning
  • 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
  • Proficiency in Unix/Linux environments, ability to navigate the system, investigate processes and logs, analyze system performance using tools such as Dynatrace
  • Experience with Databases – relational and No-SQL (Sybase, Oracle, Mongo DB)
  • Expertise with Messaging Middleware platforms (Kafka/RabbitMQ), and exposure to cloud compute platforms, including AWS
  • Proven experience with full development lifecycle and tools, including Git/Bitbucket, Jira, Jenkins, Gradle, and Maven
  • Demonstrated experience with Test-Driven Development (TDD) using tools such as JUnit and mocking frameworks
  • Ability to work in a 3rd Level advanced support capacity and supporting production environments, responding to user concerns, and taking ownership of production issues

Nice to have

  • Experience of working in financial services and understanding of equity derivative products
  • Hands-on coding experience with Python
  • Experience with UI concepts, languages and platforms including JavaScript/REACT, AngularJS, Typescript, HTML5, CSS3
  • In-depth knowledge of AWS Public Cloud products and solutions (EC2, S3, Lambda, EFS)
  • Cloud certification – AWS or Kubernetes

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