Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
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
Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on Software Engineering concepts and 5+ years applied experience
Hands-on experience coding in Java, Spring Boot, JPA, SQL, REST
Experience with event driven architecture using Kafka
Experience building RESTful backend applications using Spring Boot and solid relational enterprise databases with Oracle
Experience with version control systems, such as Git.
Strong knowledge of CI/CD e.g. Jenkins
Hands-on practical experience in system design, application development, testing and operational stability
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Solid understanding of cross cutting concerns such as Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
Familiarity with Groovy and Gradle
Experience coding in Python
Strong understanding of containerization technologies such as Kubernetes or Docker
Exposure to AI/ML technologies – especially familiarity with model training and inference
Exposure to AWS cloud technologies
