Lead Software Engineer - Java Full Stack, AI, Boot

Position: Lead Software Engineer - Java Full Stack, AI, Spring Boot
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 - Enterprise Technology team , 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 break down technical problems

Develops secure high-quality production code, and reviews and debugs code written by others

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

Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies

Adds to team culture of diversity, opportunity, inclusion, and respect

Adds to team culture of opportunity, inclusion, and respect

Evolve and apply AI in software development and automation

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.

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 on software engineering concepts and 5+ years applied experience

Experience in the Software Development Life Cycle, including hands-on development experience

Hands-on experience in Java/J2EE, REST Services, Spring Boot Strong experience writing complex SQL queries, joins, PL/SQL, functions, and stored procedures.

Hands-on experience in public cloud – Kubernetes, AWS and extensive experience with ECS, S3, RDS, Lambda, Cloud Watch, Eventbridge, and Step Functions, as well as infrastructure automation using Terraform Proficiency in designing and implementing RESTful APIs for enterprise-scale applications.

Ability to design software systems using object-oriented principles and practices.

Experience in building Decoupled Systems. Proficiency in using design patterns and technologies to decouple system components, enhancing flexibility and maintainability.

Proficiency in unit testing frameworks such as JUnit and Mockito for ensuring code quality.

Knowledge of messaging platforms such as Apache Kafka 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;…