Software Engineer III - Cloud Engineering Acceleration

In this role you design and deliver trusted market-leading software within a bold Public Cloud Engineering group at JPMorgan Chase. You work on secure, scalable solutions that support the firm’s objectives, collaborating across cross-functional teams to improve delivery, reliability, and automation. You will apply modern engineering practices and AI-assisted tooling to raise code quality and throughput while maintaining security and resiliency. This is a growth-focused opportunity to influence platform services and developer experience at scale.

• Design, develop, and troubleshoot software solutions beyond routine approaches
• Deliver secure, high-quality production code and review others’ code
• Leverage enterprise AI coding assist tools to boost quality and speed, with peer review and secure coding standards
• Utilize SDLC toolchain and automation (CI/CD, infrastructure automation) to improve value
• Produce architecture/design artifacts for platform services and complex applications with proper security and resiliency constraints
• Participate in design reviews and technical discussions to ensure scalable delivery
• Identify opportunities to automate recurring issues for operational stability
• Collaborate to evaluate/adopt tools that improve developer experience and platform quality
• Contribute to knowledge sharing (documentation, demos) to support modern engineering practices
• Promote a culture of diversity, inclusion, continuous improvement, and shared learning

• Formal training or certification on software engineering concepts and advanced applied experience
• Hands-on experience in system design, application development, testing, and operational stability
• Production-grade solutions experience with Java, Spring, Go, Python, Terraform, and/or Kubernetes
• Practical cloud-native experience on at least one major public cloud (AWS, Azure, or GCP)
• Proficiency in automation and CI/CD methods
• Understanding of agile practices, resiliency, and security fundamentals
• Hands-on experience with enterprise AI-assisted development tools and ability to validate AI outputs for correctness, performance, and security
• Knowledge of responsible AI use in engineering workflows, including data sensitivity, security and resiliency considerations
• Strong communication skills for technical and non-technical audiences
• Customer-focused mindset in evaluating technical solutions

• Strong communication
• Collaboration across teams
• Adaptability
• Java
• Spring
• Go