Java Backend Software Engineer II Kubernetes / AWS / Golang

Join one of the world's most innovative financial technology teams and make your mark on the systems that keep global commerce moving. At JPMorganChase, we invest in our engineers — giving you access to cutting-edge tools, AI capabilities, collaborative teams, and the scale to solve problems that truly matter.
As a Software Engineer II at JPMorganChase within the Cloud Payment Common Core Services Group, you will design and build the distributed, cloud-native infrastructure that underpins critical payment processing systems. You will work alongside talented engineers to deliver high-availability, AI-enhanced solutions that serve millions of customers, contributing to a team culture that values innovation, ownership, and continuous learning.
Our Cloud Payment Common Core Services Group is responsible for the foundational services that enable seamless, secure, and scalable payment experiences across the firm. You will be part of a team that thrives on solving complex engineering challenges in a fast-paced, collaborative environment — including the integration of artificial intelligence to drive smarter, faster payment experiences.
Key Responsibilities
Execute standard software solution design, development, and technical troubleshooting across cloud-native payment services built on AWS, Java, and Kotlin
Write secure, high-quality code using modern programming languages, including Java and Kotlin, with containerized deployments via Docker and Kubernetes (EKS)
Leverage enterprise-authorized AI coding assist tools to improve code quality, delivery speed, and productivity — including code generation, refactoring, unit test creation, and documentation — while validating outputs through peer review, automated testing, and secure coding standards
Design, develop, and troubleshoot with consideration of upstream and downstream systems, including event-driven architectures powered by Kafka and NoSQL data storage using MongoDB
Apply knowledge of the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities such as Jenkins and Terraform, to improve the value realized through automation
Apply technical troubleshooting to break down solutions and resolve technical problems of moderate complexity across distributed payment systems
Gather, analyze, and draw conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development
Integrate AI and machine learning models into payment services to enable intelligent automation, anomaly detection, and predictive capabilities in collaboration with data science and AI engineering teams
Learn and apply system processes, methodologies, and skills for the development of secure, stable code and systems within an agile, cross-functional environment
Required Qualifications, Capabilities, and Skills
Formal training or certification on software engineering concepts and 2+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience developing, debugging, and maintaining code in a large corporate environment using one or more modern programming languages — including Java or Kotlin — and database querying languages, with working knowledge of MongoDB
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation), with demonstrated ability to critically evaluate and validate AI-generated outputs
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs and outputs, and adherence to resiliency and security expectations
Experience across the full Software Development Life Cycle, including design, development, testing, deployment, and operational support
Exposure to agile methodologies and practices such as CI/CD, application resiliency, and security
Experience with containerization technologies including Docker and Kubernetes (EKS) on AWS
Familiarity with infrastructure-as-code tooling, specifically Terraform, and event-driven messaging systems such as Kafka
Preferred Qualifications, Capabilities, and Skills
Experience with Kotlin for backend service development and CI/CD pipeline tooling, particularly Jenkins
Hands-on experience integrating AI or machine learning APIs, frameworks, or models — such as large language models or predictive analytics — into production applications
Familiarity with AI and machine learning platforms or tools on AWS (e.g., SageMaker) for model deployment and monitoring
Experience in payments, financial services, or other high-availability, high-throughput domains
Understanding of cloud security best practices and payment compliance standards