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Lead Software Engineer - Cloud

Job in Plano, Collin County, Texas, 75086, USA
Listing for: JPMorgan Chase
Full Time, Seasonal/Temporary position
Listed on 2026-10-15
Job specializations:
  • Software Development
    Software Engineer, Software Architect, DevOps, Cloud Engineer - Software
Salary/Wage Range or Industry Benchmark: 140000 - 190000 USD Yearly USD 140000.00 190000.00 YEAR
Job Description & How to Apply Below

Lead Software Engineer - Cloud

Plano, TX, United States

Job Information
  • Job Identification
  • Job Category Software Engineering
  • Business Unit Corporate Sector
  • Posting Date 10/09/2026, 07:10 PM
  • Locations 8181 Communications Pkwy Bldg F, Plano, TX, 75024, US
  • Job Schedule Full time
  • Job Shift Day
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 JPMorgan

Chase within the Corporate Sector – Infrastructure Platforms, you are 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
  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
  • Develops secure and high-quality production code, and reviews and debugs code written by others
  • 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.
  • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
  • Lead the design and implementation of platform services and infrastructure with an emphasis on architecture, integration patterns, and system reliability
  • Develop, review, debug, and maintain secure, high-quality production code, applying engineering judgment to ensure it meets our standards regardless of whether it originates from you, a teammate, or an approved artificial intelligence tool
  • Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability across applications and systems
  • Lead design reviews and architecture discussions to evaluate solutions, strengthen technical quality, and support scalable delivery
  • Partner with platform architecture, trust and authorization teams, and downstream IAM consumers to land coherent designs across organizational seams;
    Lead communities of practice across Software Engineering to increase awareness and adoption of new and leading-edge technologies
  • Mentoring and coaching engineers through technical guidance
Required qualifications, capabilities, and skills
  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • 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…
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