Identify and implement automation and sustainable fixes for recurring issues to improve the operational stability and resiliency of applications and systems
Partner in technical evaluations with external vendors, startups, and internal teams by contributing to outcomes-oriented reviews of architecture, technical capabilities, and platform fit
Contribute to communities of practice across software engineering by sharing knowledge, supporting adoption of modern technologies, and promoting engineering best practices
Leverage 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
Apply 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
Support a team culture that values diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and expanding applied experience
Hands-on experience contributing to system design, application development, testing, and production support and operational stability
Strong proficiency in Java and common frameworks such as Spring Boot, Hibernate, JPA, and Spring Kafka
Working knowledge of microservices architectures and Kafka or other messaging and event-driven architectures
Experience with automation and continuous delivery practices, including build and test automation and CI/CD pipelines
Proficiency across the Software Development Life Cycle including requirements, design, implementation, testing, release, and support
Good understanding of Agile delivery, including CI/CD concepts, application resiliency, and secure engineering practices
Demonstrated ability to develop within at least one technical discipline such as cloud, data, or mobile
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 additional languages and frameworks such as .NET (C#) and Python
Familiarity with modern front-end technologies such as React and/or Angular
Experience with container platforms and platform-as-a-service environments, including Kubernetes and Cloud Foundry
Familiarity with Generative AI tools and patterns such as prompt engineering, retrieval-augmented generation, vector search and embeddings, and safe and secure adoption practices
