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.
Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets to drive continuous improvement of applications and systems
Proactively identify hidden problems and patterns in data and use insights to improve coding hygiene and system architecture
Contribute to software engineering communities of practice and events that explore new and emerging technologies
Ability to independently work on modules with minimal support
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Experience in full stack software engineering, with a track record of developing and deploying business critical applications in production.
Proficiency in Java, Spring Boot, SQL with the ability to learn additional programming languages as needed
Experience with React and other front end technologies
Experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of incorporating AI use in engineering workflows, including data sensitivity considerations, curation of inputs, measuring performance, model fine tuning and adherence to resiliency and security expectations;
Knowledge of cloud-based development
Experience developing, debugging, and maintaining code in a large corporate environment, including use of database querying languages
Overall knowledge of the Software Development Life Cycle, including agile practices such as CI/CD, application resiliency, and security
Preferred qualifications, capabilities, and skills
Experience in programming languages like Python for model development, experimentation, building Agents and integration with Agentic workflows
Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
