As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking's Technology, BBAO team, you will design, build, and support scalable backend services, APIs, and modern UI applications that enable seamless customer acquisition and account origination journeys. You’ll contribute across the full software development lifecycle, partner with cross-functional stakeholders, and apply strong engineering practices (testing, CI/CD, observability, and secure development) to deliver resilient, high-quality solutions. You’ll also have opportunities to contribute to AI-enabled capabilities and agent-driven tools that improve customer and employee experiences.

Job Responsibilities:

• 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.
• Design, develop, and maintain full stack solutions, including Java/Spring Boot backend services, RESTful microservices, and modern UI applications using React or Angular.
• Build secure, high-performing APIs and integrations; contribute to service reliability, resiliency, and performance tuning.
• Collaborate daily with Product, Design, and Data & Analytics to refine requirements, estimate work, and deliver iteratively using Agile practices.
• Write clean, maintainable code with strong unit/integration test coverage; participate in code reviews and design discussions.
• Support and improve CI/CD pipelines and engineering automation; champion best practices in quality and developer productivity.
• Contribute to production support: troubleshooting, incident triage, root-cause analysis, and preventative improvements.
• Participate in application, data, and infrastructure architecture conversations and help evolve platform standards.

Required Qualifications, capabilities, and skills:

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• 8+ years of hands-on software engineering experience delivering production applications, with a strong focus on backend and UI development.
• 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
• Proficiency with modern UI frameworks such as Angular or React (to effectively partner on end-to-end delivery).
• Proficiency in Java and Spring Boot, including building and operating REST APIs and microservices.
• Experience with modern Agile delivery practices (Scrum/Kanban), CI/CD, and DevOps-aligned development (automated quality gates, release pipelines).
• Experience with cloud and/or container platforms (e.g., AWS or Cloud Foundry, Docker/Kubernetes).
• Working experience with databases: Oracle and/or NoSQL datastores such as Cassandra or MongoDB (data modeling, query performance, reliability).
• Experience with observability tooling (metrics, logs, traces) and production readiness practices.
• Experience with UAT and/or accessibility testing. 

Preferred qualifications, capabilities, and skills: 

• Interest and ability to build agent-style tools/workflows that execute multi-step tasks using tools/APIs (e.g., orchestration, routing, workflow automation).
• Familiarity with reliability patterns for LLM applications, such as:
  • Tool/function calling and structured outputs
  • Prompt iteration and evaluation
  • Grounding approaches such as RAG (retrieval-augmented generation)
• Awareness of responsible AI fundamentals: privacy-by-design, safe handling of sensitive data, and validating outputs for correctness and appropriate use.
• Interest in operationalizing AI-enabled components: monitoring quality, latency, and cost.
• Experience with automated functional testing tools such as Cucumber (or equivalent) and strong testing discipline (TDD is a plus).
• Strong communication skills (written and verbal) and ability to work effectively with cross-functional partners.