Leads software engineering within an agile Payments Technology team, building secure, scalable, high-throughput Java applications. Provides technical guidance, develops and reviews production code, drives AI-assisted engineering practices, improves SDLC automation, and influences product design and technical operations. Requires expertise in Java, Spring Boot, Kafka, cloud-native applications, distributed systems, performance tuning, and responsible AI use. Preferred experience includes Kafka Streams, microservices, Kubernetes, Apigee, behavior-driven design, agentic workflows, and production agentic AI.
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 Payments Technology, 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 Payments Technology, 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, contractors, and vendors
- 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
- Proven expertise in building large scale & high throughput application
- Proficient in Java, Spring Boot, Kafka & cloud native applications
- Proficient in distributed systems and parallel processing
- Proficient in Performance Analysis and Tuning (Working knowledge of Java based Profiling and Monitoring Tools)
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
- Ability to tackle design and functionality problems independently with little to no oversight
Preferred qualifications, capabilities, and skills
- Working experience of Kafka Streams, Even Driven Architecture, Microservices Architecture
- Good understanding of Behavior driven design
- Understanding of Kubernetes, APIGEE gateway and cloud services
- Working knowledge of agentic workflows
- Proven production deliverables in agentic AI
- Hands-on across the modern AI toolchain
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