Lead Software Engineer - Python/ Java/ AWS at JPMorganChase – Jersey City | Jobyssey

Job Summary

As a Lead Software Engineer at JPMorganChase within Asset and Wealth Management Advisor & Investment Technology, you will be a hands-on technical developer responsible for designing, building, and operating critical technology solutions aligned to firm objectives. You will contribute directly to production code, raise engineering standards through review and mentorship, and drive reliability, security, and operational excellence across the software lifecycle. You will also help the team adopt enterprise-authorized AI-assisted engineering practices in a responsible, controlled manner to improve quality and delivery outcomes.

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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
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

Required Qualifications, Capabilities, and Skills

Formal training or certification on software engineering concepts and 5+ years applied software engineering experience
Strong knowledge of modern architectures and patterns, including microservices, REST APIs, event-driven design, and NoSQL data stores.
Experience building cloud-native applications and services; working knowledge of AWS (or comparable cloud platform).
Demonstrated experience delivering at least two large, complex applications end-to-end, from initial build through production delivery (ideally in a large financial institution or world-class product engineering organization).
Working knowledge of CI/CD and DevOps toolchains, observability/monitoring practices, and a test-driven approach within agile delivery models.
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
Advanced understanding of application resiliency and security principles and practices.
Strong technical documentation skills (e.g., API documentation with 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 (coding, code review, test acceleration, troubleshooting) and setting clear team expectations for validating AI outputs for correctness, performance, and security.

Preferred Qualifications, Capabilities, and Skills

Experience designing and building high-availability system architectures.
Demonstrated ability to drive engineering process improvements and change adoption, including culture and ways-of-working changes.

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