Lead Software Engineer - Java - Equities - Vice President

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

Lead Software Engineer for JPMorgan Chase's Equity Derivatives Group, focusing on creating and transforming technology products using modern practices. The role involves collaborating with a global team, driving adoption of AI-assisted engineering practices, and ensuring responsible AI use within development workflows. Responsibilities include software solution execution, technical troubleshooting, collaboration with various stakeholders, and contributing to team culture.

What you'd actually do

  1. Execute 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. Collaborate with engineers, product owners, traders, sales and Quants to develop best in class software solutions for our Equity Derivatives platform
  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. Work within a team of software engineers analyzing and implementing business requirements within an Agile/Scrum environment

Skills

Required

  • Formal training or certification on software engineering concepts and 3+ years applied experience.
  • Experience with Java/JVM and the Java/JVM ecosystem, Spring boot framework
  • Experience of TypeScript/React development
  • 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 with object-oriented design and micro-service architectures and best practices
  • Proficient in all aspects of the Software Development Life Cycle
  • Good understanding of software design patterns and clean code practices
  • Strong written and oral communication skills
  • Proficiency in automation and continuous delivery methods
  • Advanced understanding of agile methodologies, CI/CD, Application Resiliency, and Security
  • Experience partnering with product and engineering teams

Nice to have

  • Experience working on financial systems is a plus
  • Experience with finance / derivative products. Knowledge of Convertible Bonds is a plus
  • Experience of publish/subscribe messaging protocols such as Kafka , AMQP, AMPS
  • Experience with C#/WPF desktop application programming
  • Exposure to Relational Database Management Systems (Sybase)
  • Experience of Cloud technologies
  • Exceptional client relationship skills including the ability to discover true requirements underlying feature requests, recommend alternative technical approaches, drive efforts to meet committed timelines with optimal solutions

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