Lead Software Engineer - Java/Spring

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

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 JPMorganChase within the Commercial & Investment Bank, 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
• 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
• Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on Software Engineering concepts and 5+ years applied experience
• Proficient in coding in Java, Spring Boot, JPA, SQL, REST
• Experience with event driven architecture using Kafka
• Experience building RESTful backend applications (Spring Boot) and solid relational enterprise databases (Oracle) experience 
• Experience with version control systems, such as Git.
• Strong knowledge of CI/CD e.g. Jenkins
• Hands-on practical experience in system design, application development, testing and operational stability
• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
• 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

• Solid understanding of cross cutting concerns such as Application Resiliency, and Security

• Familiarity with Groovy and Gradle
• Strong understanding of containerization technologies such as Kubernetes or Docker
• Exposure to AI/ML technologies – especially familiarity with model training and inference
• Exposure to AWS cloud technologies