Cloud Software Engineer II

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Software Engineer II at JPMorgan Chase on the Pathfinder team, you are an active contributor in an agile team that builds and delivers trusted technology products in a secure, stable, and scalable way. The Pathfinder team owns the Application and Infrastructure Blueprint ecosystem across JPMC via the Kickstart Platform, enabling developers to build and deploy modern apps on Public Cloud (AWS) and Private Cloud (GAP/GKP).

Job responsibilities

• Writes, tests, and deploys production-quality code with increasing autonomy

• Implements solutions across the Application Blueprint and Infrastructure Blueprint ecosystem

• Contributes to code review, pair programming, and shared design discussions

• Identifies opportunities for continuous improvement in team practices and delivery workflows

• Shares knowledge with peers and newer team members while continuing to learn from seniors

• Adds to team culture of diversity, opportunity, inclusion, and respect

• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and applied experience

• Hands-on experience in one or more languages such as Java, Python or Go

• Exposure to cloud platforms (AWS) and Infrastructure-as-Code tooling such as Terraform

• Familiarity with CI/CD practices and the Software Development Lifecycle

• Understanding of agile methodologies and consistent delivery within a sprint-based model

• 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/outputs, and adherence to resiliency and security expectations.

Preferred qualifications, capabilities, and skills

• Experience with container and orchestration technologies such as AWS Lambda, Kubernetes (EKS), or AWS ECS