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Lead Software Engineer - Java, Python, Boot, AWS

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, Java Developer, DevOps, Backend Developer
Salary/Wage Range or Industry Benchmark: 150000 - 190000 USD Yearly USD 150000.00 190000.00 YEAR
Job Description & How to Apply Below
Position: Lead Software Engineer - Java, Python, Spring Boot, AWS

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 Technology, 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 software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness
  • 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
  • Designs, implements, and deploys secure, resilient, highly scalable, fault-tolerant services that integrate with enterprise systems; ensures functional, performance, scalability, security, governance, and auditability requirements are met
  • Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
  • Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
  • Applies backend infrastructure patterns (e.g., load balancing, autoscaling) and implements monitoring/alerting for performance, scalability, availability, and reliability
  • Drives consistent validation standards (secure coding, peer review, automated testing) and promotes reuse of effective patterns across the team
  • Leads and mentors junior team members in a high-pressure delivery environment; manages multiple deliverables across business groups and strengthens stakeholder relationships
  • Defines and implements guardrails and evaluation approaches for agentic AI used in production applications to deliver business value while meeting quality, safety, latency, and cost requirements
  • Collaborates with LOB users, SMEs, architects, DBAs, and system administrators to design solutions, manage enhancements, and resolve issues
Required qualifications, capabilities, and skills:
  • Formal training or certification in computer science or software engineering concepts and 5+ years of applied experience
  • 10+ years of hands-on software development experience in large-scale distributed systems, primarily in Java, modern Java/Spring Boot microservices, and Python
  • Hands-on practical experience in system design, application development, testing, and operational stability
  • Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations;…
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