Lead Software Engineer - Full-Stack Java, React & AI

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 Asset & Wealth Management, 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:

• Own application components end-to-end across the SDLC, including architecture, build, test, deploy, observability, and production support.
• Partner with product, design, business, and engineering stakeholders to translate complex requirements into actionable technical plans.
• Design and deliver secure, resilient, high-performance services and user experiences for enterprise-scale managed-account platforms.
• Lead technical discovery, solution design, complexity estimation, and implementation for high-impact initiatives.
• Coach engineers via code reviews, pairing, technical stories, and pragmatic engineering standards.
• Drive adoption of enterprise-approved AI-assisted engineering practices to improve quality, speed, and operational outcomes.
• Apply appropriate architectural patterns (DDD, microservices, event-driven, cloud-native) where they add clear value.
• Use AI coding tools (e.g., Claude Code, GitHub Copilot) to accelerate understanding, delivery, testing, debugging, docs, and modernization.
• Create high-quality prompts and reusable guidance that provide AI agents with necessary business, architecture, and repo context.
• Validate all AI-generated changes (secure coding, peer review, automated testing), keep edits focused, protect sensitive data, and remain accountable for outcomes.
• Improve CI/CD, test automation, developer experience, and operational readiness by reducing manual effort and tightening feedback loops.

Required qualifications, capabilities, and skills:

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 10+ years of hands-on enterprise software engineering experience using Java, J2EE, REST APIs, Spring Boot, Hibernate or another ORM, microservices, and relational databases such as Oracle or Sybase.
• 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 experience designing highly scalable, secure, resilient, and observable distributed systems.
• Deep understanding of object-oriented design, Domain-Driven Design, microservices patterns, client-server architecture, and modern cloud-native architectures.
• Strong test-driven development skills and practical experience with JUnit, Mockito, Cucumber, or comparable automation frameworks.
• Proficiency in SQL, complex queries, joins, stored procedures, views, indexes, and database change management; Liquibase experience is beneficial.
• Hands-on experience building CI/CD pipelines using Git, Maven, Jenkins, SonarQube, or equivalent tools.
• Experience with Agile software delivery and a track record of leading technical work in collaborative, cross-functional teams.
• Strong analytical and problem-solving skills, with excellent attention to detail and the ability to communicate complex ideas clearly.

Preferred qualifications, capabilities, and skills:

• Front-end development with React, Angular, TypeScript, JavaScript, HTML, and state-management libraries such as Redux.
• Event-driven processing with Kafka or a comparable messaging platform.
• Observability and monitoring with Dynatrace, Splunk, Grafana, AppDynamics, or similar technologies.
• Cloud Foundry or other public/private cloud platforms.
• Practical experience with Claude Code, GitHub Copilot, or similar AI coding assistants, including repository-level context, agentic workflows, automated test generation, code review, and documentation.
• Experience defining guardrails and validation practices for responsible AI-assisted development, including human review, secure data handling, focused changes, and measurable test coverage.
• Banking, wealth management, managed accounts, or broader financial-services domain knowledge.