Software Engineer III - Java AWS Kafka

Job overview

Core responsibilities

• Execute software solutions, design, development, and technical troubleshooting across multiple technical areas.
• Create secure, high-quality production code and maintain algorithms that work synchronously with appropriate systems.
• Produce architecture and design artifacts for complex applications and ensure design constraints are met by the code.
• Analyze large, diverse data sets and develop visualizations and reporting to support continuous improvement.
• Identify hidden problems and patterns in data to improve coding hygiene and system architecture.
• Contribute to software engineering communities of practice and emerging technology events.

AI-assisted development

• Use enterprise-authorized AI coding assistants for code generation, refactoring, unit tests, documentation, and other complex deliverables.
• Validate AI-generated outputs through peer review, automated testing, and secure coding standards.
• Apply Software Development Life Cycle tools and AI-assisted automation capabilities to improve engineering productivity and value.
• Share reusable patterns and learnings that improve broader team effectiveness.

Required qualifications

• Formal training or certification in software engineering concepts and 3+ years of applied experience.
• Hands-on experience with Java, Spring, AWS, Kafka, event streaming, Hibernate, JPA, SQL, and NoSQL databases.
• Practical experience in system design, application development, testing, and operational stability.
• Proficiency in coding in one or more languages and experience maintaining code in a large corporate environment.
• Overall knowledge of the Software Development Life Cycle and agile methodologies including CI/CD, resiliency, and security.
• Knowledge of software applications and technical processes within a technical discipline.

Responsible AI qualifications

• Hands-on experience with enterprise-authorized AI-assisted software development tools for coding, testing, troubleshooting, or documentation.
• Ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of data sensitivity, secure input and output handling, and responsible AI use in engineering workflows.
• Ability to guide peers on safe and effective AI-assisted development practices.

Preferred qualifications

• Familiarity with modern front-end technologies is preferred.
• Exposure to cloud technologies is preferred.