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Lead Software Engineer - Java, AWS

Lead Software Engineer - Java, AWS

JP Morgan Chase & Co.
5-7 Years
Early Applicant
  • Posted 2 months ago
  • Be among the first 20 applicants

Job Description

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 JPMorganChase within the Asset and Wealth Management, 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

  • EServe as a hands-on technical contributor delivering critical solutions across business functions aligned to firm objectives.
  • Develop secure, high-quality production code review, debug, and provide feedback on code written by others.
  • Identify recurring issues and implement automation and/or durable remediation to improve operational stability and resiliency.
  • Lead evaluation sessions with technology partners to drive outcome-oriented review and validation of architectural designs and trade-offs.
  • Lead and contribute to communities of practice across Software Engineering to promote adoption of leading-edge technologies and engineering standards.
  • 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.

Required qualifications, capabilities, and skills

  • Formal training or certification in software engineering concepts with 5+ years of applied engineering experience.
  • Strong knowledge of modern architectures, including microservices, REST APIs, NoSQL data stores, and event-driven patterns.
  • Experience building cloud-native applications and services. Working knowledge of AWS (or a comparable cloud platform).
  • Demonstrated experience delivering at least two large, complex applications end-to-end (from initial build through production delivery), ideally within a large financial institution or a world-class product engineering organization.
  • Working knowledge of CI/CD, DevOps toolchains, software monitoring/observability, and a test-driven approach within agile delivery.
  • Strong collaboration skills, with the ability to execute multiple parallel workstreams with engineers, analysts, and cross-functional partners.
  • Advanced understanding of application resiliency and security principles and practices. Strong technical documentation skills (e.g., API documentation using OpenAPI/Swagger).
  • Familiarity with AI concepts and developer productivity tools (e.g., Microsoft Copilot or similar).
  • 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
Preferred qualifications, capabilities, and skills
  • Experience designing and building high-availability system architectures.
  • Experience driving engineering process improvements and change adoption (including associated culture and ways-of-working changes).

Key Skills

AI concepts

Microsoft Copilot

application resiliency

DevOps toolchains

CI CD

OpenAPI Swagger

observability

NoSQL data stores

AI-assisted software development tools

software monitoring

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