Sr Lead Software Engineer- Java Back End, AWS

JPMorgan Chase & Co.; Plano (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

JPMorganChase is seeking a Senior Lead Software Engineer to join the Asset & Wealth Management technology team. You will guide cross-functional engineering efforts, deliver secure, scalable backend services, and drive AI-enabled development practices across teams.

You will tackle complex problems with deep technical expertise in Java/Spring and AWS, while mentoring engineers and shaping governance of AI-enabled engineering operating models.

Qualifications

• Formal training or certification on software engineering concepts.
• Experience leading large or cross-functional teams of technologists.
• Hands-on backend experience with Java/Spring or Python and data modeling across relational/NoSQL databases.

Responsibilities

• Provide technical guidance to business and technical teams.
• Develop secure, high-quality production code and review others' code.
• Promote AI-assisted engineering practices to improve quality and delivery speed.
• Leverage SDLC tools to improve automation and release readiness.

Skills

Java backend

Python

SQL databases

AWS

Kafka

CI/CD

Leadership

Communication

AI governance

Secure coding

Distributed systems

Education

Software engineering training

Tools

Spring Boot

Hibernate/JPA

Job description

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

• 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.