Lead Software Engineer - Full-Stack Java, React & AI
JPMorgan Chase · Plano, United States · 14d ago
Staff / Lead / PrincipalOn-siteSoftware Engineering
J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.
Our Asset and Wealth Management division is driven by innovators like you who are driven to create technology solutions that make us work more efficiently and help our businesses grow. It’s our mission to efficiently take care of our clients’ wealth, helping them get, and remain properly invested. Our team of agile technologists thrive in a cloud-native environment that values continuous learning using a data-centric approach in developing innovative technology solutions.
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:
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.