Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Sr Lead Software Engineer at JPMorgan Chase within the Consumer & Community Banking, 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. Drive significant business impact through your capabilities and contributions and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

 

Job responsibilities

• Designs software solutions, including complex debugging and technical troubleshooting, using strong problem-solving and engineering judgment.
• 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.
• Creates architecture and design artifacts for complex applications and ensure implementations adhere to defined design constraints.
• Analyzes large, diverse datasets and produce visualizations and reporting that drive continuous improvement of systems and applications.
• Identifies hidden problems and data patterns to improve coding hygiene, observability, and system architecture.
• Improves service reliability through automated testing, CI/CD, resiliency patterns, and secure-by-design engineering practices.
• Develops reusable services and shared components that enable consistency, scalability, and faster delivery across teams.
• Evaluates and adopts emerging technologies through communities of practice to deliver measurable engineering and business value.
• Collaborates within agile teams to deliver outcomes with clear technical ownership and delivery accountability.

 

Required qualifications, capabilities, and skills

• 5+ years of application development experience delivering production-grade software.
• Hands-on experience with system design, application development, testing, and operational stability in production environments.
• Experience developing, debugging, and maintaining code in a large enterprise environment using modern programming languages and database query languages.
• Proficiency in multiple modern programming languages, with demonstrated depth in at least one.
• Demonstrated experience building distributed applications in Java 8+, including RESTful APIs, microservices, and Spring Boot.
• Experience delivering multi-threaded, high-throughput, mission-critical systems with performance and latency considerations.
• 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.
• Experience with cloud-native application development (e.g., GCP or private cloud).

 

Preferred qualifications, capabilities, and skills

• Experience deploying and supporting applications on AWS.
• Experience with messaging technologies such as Kafka or IBM MQ.
• Experience with database platforms such as Cassandra, Oracle, Aurora, or DynamoDB.
• Experience with behavior-driven development tools such as Cucumber.
• Experience configuring and operating continuous integration pipelines such as Jenkins.
• Proficiency with Python and Linux shell scripting.
• Experience building open-source libraries or internal shared libraries adopted by multiple engineering teams.