Architect and deploy cloud-native solutions on AWS, applying best practices for scalability, resiliency, and cost efficiency
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
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 through automation
Collaborate with cross-functional teams including front-office technology, data engineering, and business stakeholders to translate complex requirements into scalable, maintainable solutions
Participate in Agile ceremonies and contribute to sprint-based delivery cycles, supporting continuous integration and deployment practices
Troubleshoot and resolve production issues across multi-layered distributed systems, applying root cause analysis to improve system resilience and prevent recurrence
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
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability using Java and Spring Framework
Experience developing, debugging, and maintaining code in a large corporate environment with demonstrated proficiency in Java backend development and Oracle/SQL database querying
Hands-on experience with Apache Kafka or equivalent event-driven messaging platforms in a distributed systems environment
Experience designing and deploying applications on AWS or equivalent cloud platforms, with an understanding of cloud-native architecture principles
Hands-on experience using enterprise-authorized AI-assisted software development tools — such as 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 exposure to Agile methodologies and practices such as continuous integration and delivery, application resiliency, and security
Preferred qualifications, capabilities, and skills
Familiarity with containerization and orchestration technologies such as Docker and Kubernetes in a cloud-native deployment model
