Java AI Lead Software Engineer, Automation and Reliability

Lead Software Engineer

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 Consumer & Community Banking Deposits 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

• Develops and codes in Java with some Python as an individual contributor solving complex technical issues and driving resolution across applications, services and platforms
• 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.
• Operates as a senior hands-on individual contributor, writing code, solving complex technical issues, and driving resolution across applications, services, and platforms
• Leads the design, development and modernization of software solutions that improve reliability, scalability, automation and operational efficiency across complex technology platforms
• Builds, enhances and maintains automation frameworks, AI-driven engineering solutions, internal platforms and reusable tools for engineering teams
• Supports performance testing, resiliency testing, chaos testing, and controlled failure scenarios to validate system behavior under production-like conditions
• Leads engineers through code reviews, technical guidance, problem-solving support, and promotion of strong engineering practices
• Communicates technical strategy, reliability risks, resolution plans, and delivery progress clearly to engineering teams, product partners, and leadership
• Defines and promotes release readiness checks, quality gates, test coverage expectations, reliability standards, and validation approaches for cloud-native platforms

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Advanced proficiency in programming languages such as Java (Primary) and Python (Secondary)
• 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
• Strong hands-on software engineering experience with the ability to design, code, debug, test, and resolve complex technical problems independently
• Possess a strong ownership mindset with a track record of solving difficult problems, improving engineering practices, and driving technical issues to closure across complex environments
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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…