Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Advanced Java experience, including building Spring Boot–based services in a microservices architecture
Experience with Java (Core & EE, Spring Boot, Spring MVC, Spring Cloud)
Practical experience delivering system design, application development, automated testing, and operational stability for production systems
Hands-on experience building cloud-native applications on Amazon Web Services (e.g., compute, storage, database, container, and serverless services)
Experience with continuous integration and delivery and modern build/version control practices (e.g., Git, Maven/Gradle, and pipeline automation)
Proficiency with automated testing approaches and frameworks (e.g., JUnit and mocking frameworks) and a strong quality-first mindset
Experience with relational databases and SQL, including data modeling and performance considerations for high-throughput systems
Knowledge of messaging and integration patterns, including event streaming technologies such as Kafka
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
Preferred qualifications, capabilities, and skills
Basic Python scripting knowledge
Experience building and deploying agentic or AI-assisted workflows, including evaluation and human-in-the-loop validation patterns
Experience with infrastructure as code and cloud provisioning automation (e.g., Terraform, CloudFormation, or AWS CDK)
Experience with containerization and orchestration (e.g., Docker and Kubernetes) for scalable service operations
