Execute standard software solutions through design, development, and technical troubleshooting across multiple system components
Write secure, high-quality code using at least one programming language with limited guidance
Design, develop, and troubleshoot with consideration of upstream and downstream systems and their technical implications
Apply technical troubleshooting skills to break down and solve technical problems of basic complexity
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
Learn and apply system processes, methodologies, and best practices for the development of secure, stable code and systems
Contribute to a team culture of diversity, opportunity, inclusion, and respect
Leverages 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
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
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and expanding applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience developing, debugging, and maintaining code in a large-scale environment using one or more modern programming languages and database querying languages
Demonstrable ability to code in one or more languages
Experience across the full Software Development Life Cycle
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Exposure to agile methodologies such as continuous integration/continuous delivery, application resiliency, and security practices
Emerging knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
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
Familiarity with modern front-end technologies
Exposure to cloud technologies
Experience with Java, Spring Boot, Kafka, or Kubernetes in an enterprise environment Familiarity with container orchestration and microservices architecture patterns
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