Lead Software Engineer - Java/Microservices/AWS

JPMorganChase•Wilmington, NC

About The Position

As a Lead Software Engineer on JPMorganChase’s Consumer and Community Banking team, you will contribute to an agile team building, enhancing, and delivering secure, stable, scalable technology products. You will develop and deliver critical technology solutions across technical areas and business functions in support of the firm’s business objectives. The role includes hands-on software engineering and technical leadership, with a focus on Java, microservices, AWS, and responsible use of AI-assisted engineering tools.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Practical experience delivering system design, application development, testing, and operational stability.
Advanced proficiency in one or more programming languages and technologies, including Java, Spring Boot, microservices, Kafka, AWS, and AI.
Experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, or troubleshooting.
Ability to set team expectations for validating AI outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations.
Experience coaching engineers on safe, compliant adoption of AI-assisted tools in delivery practices.
Proficiency in all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies, including CI/CD, application resiliency, and security.
In-depth knowledge of the financial services industry and its IT systems.
Practical cloud-native experience.

Nice To Haves

Experience or knowledge related to emerging technologies.

Responsibilities

Design, develop, and troubleshoot creative software solutions, addressing technical problems beyond routine or conventional approaches.
Develop secure, high-quality production code, and review and debug code written by others.
Lead adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.
Establish standards for validating AI-assisted work, including secure coding, peer review, and automated testing, and promote reuse of effective team patterns.
Use Software Development Life Cycle tools, including authorized AI-assisted development and automation capabilities, to improve the value of automation.
Identify opportunities to eliminate or automate recurring issues and improve application and system stability.
Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs, technical credentials, and applicability to existing systems and information architecture.

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