at J.P. Morgan
Bulge Bracket Investment BanksPosted 9 days ago
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**Sr. Lead Software Engineer - Java Backend, AWS**: Lead an agile team, driving tech products' enhancements and delivery. Key responsibilities include: providing technical guidance, writing secure, high-quality code, promoting AI-assisted practices, and influencing product design. Required: 5+ years of software engineering experience, expert-level Java, AWS (ECS/EKS, Lambda, Aurora), Apache Kafka, strong SQL, and CI/CD skills. Strong communicator, proven experience leading tool adoption, and understanding of AI risks. Preferred: regulated environment experience.
- Compensation
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- City
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- Country
- United States
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Full Job Description
Location: Plano, TX, United States
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 Senior 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. 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
As a Senior 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. 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
- Regularly provides technical guidance and direction to support the business and its technical teams
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- Serves as a function-wide subject matter expert in one or more areas of focus
- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Experience developing or leading large or cross-functional teams of technologists
- Expert-level backend engineering in Java (Spring Boot, Spring Integration, Hibernate/JPA) and/or Python; strong SQL and data modeling across relational and NoSQL systems.
- Deep AWS experience: ECS/EKS, Lambda, Aurora, DynamoDB, S3, Glue, CloudWatch able to design, deploy, and operate cloud-native systems end-to-end.
- Hands-on Apache Kafka experience: topic design, consumer group management, exactly-once delivery, and stream processing patterns.
- Solid grasp of distributed systems fundamentals: consistency models, CAP trade-offs, idempotency, distributed transactions, and failure modes.
- Strong CI/CD and test engineering practice: you build the pipelines and write tests alongside the team.
- Excellent communicator able to move fluidly between engineering teams and business/operations stakeholders in the same conversation.
- Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
- Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
Preferred qualifications, capabilities, and skills
Proven experience delivering in a regulated environment (financial services preferred) with working knowledge of audit, entitlement, and compliance requirements.
Drive significant business impact and tackle a diverse array of challenges that span multiple technologies and applications
