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As a Lead Software Engineer at JPMorganChase 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, CloudWatch, 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; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
- Familiarity with modern front-end technologies like React JS, JavaScript, typescript
- Experience with SQL performance tuning, JDBC, and ORM frameworks (Hibernate/JPA)
- Familiarity with DevOps practices, containerization and orchestration
- Exposure to AI systems such as Copilot, Claude