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Lead Software Engineer - Backend Java, Boot

Job in Plano, Collin County, Texas, 75086, USA
Listing for: JPMorgan Chase & Co.
Full Time position
Listed on 2026-10-05
Job specializations:
  • Software Development
    Software Engineer, Backend Developer, Full Stack Developer, Cloud Engineer - Software
Salary/Wage Range or Industry Benchmark: 120000 - 180000 USD Yearly USD 120000.00 180000.00 YEAR
Job Description & How to Apply Below
Position: Lead Software Engineer - Backend Java, Spring Boot

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Corporate Sector, youare an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job Responsibilities:
  • Execute creative software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems.
  • Deliver end-to-end solutions in the form of cloud-native, microservices-based applications, leveraging the latest technologies and best industry practices.
  • Use domain modeling techniques to build best-in-class business products, structuring software for clarity, testability, and evolution.
  • Develops secure high-quality production code, and reviews and debugs code written by others.
  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
  • Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
  • 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.
  • 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.
  • Continuously update technologies and patterns to keep systems current.
  • Design and build solutions that avoid single points of failure using scalable architectural patterns.
  • Design and build scalable, secure, and reliable solutions by leveraging modern architectural patterns that ensure zero-downtime releases and optimize data performance.
Required qualifications, capabilities, and skills:
  • Formal training or certification on software engineering concepts and 5+ years of hands-on experience in software engineering, including system design, application development, testing, and operational support.
  • Proficiency in back-end technologies (e.g., Java, Springboot, Node.js ) with experience building microservices-based applications; for full-stack roles, proficiency also includes front-end technologies (e.g., HTML, CSS, JavaScript, Typescript, React, Angular).
  • Demonstrated ability to independently solve design and functionality challenges with minimal supervision.
  • Experience working with cloud platforms (e.g., AWS, Azure, GCP), distributed systems, and web technologies, including RESTful APIs and web services, Web Sockets, and JSON.
  • Hands-on experience designing and building scalable applications using SQL and No

    SQL databases.
  • 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,…
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