Description
We are seeking an Associate Software Engineer to join the Visa Resolve Online (VROL) Engineering team. You will contribute to the development of next-generation AI solutions that transform dispute management through Generative AI, Machine Learning, Conversational AI, Intelligent Document Processing, and Developer Productivity tools.
As a key member of the delivery execution team, you will create scalable designs, write maintainable code, reusable components/services/utilities, and test cases.
Responsibilities:
- Work independently on defined problems and features
- Apply established architectural patterns with good technical judgment
- Take ownership of code quality, testing, and production reliability
- Partner with GenAI agents for code generation, refactoring, and automated testing
- Experiment in Visa’s AI sandbox, where multiple GenAI models and frameworks are available for employee-driven innovation
- Apply Generative AI models to API integration challenges, anomaly detection, and intelligent automation
- Collaborate actively within a cross-functional team
- Translate functional requirements into clean, efficient, and maintainable code
- Write high-quality, readable, and testable code aligned with defined engineering standards
- Develop and maintain automated unit and component tests
- Identify and address defects, performance issues, and reliability gaps in owned areas
- Participate in production releases and operational readiness activities
- Work closely with Product Managers, DevOps, and peer engineers
Qualifications:
- Bachelor's degree in computer science, Engineering, Information Systems, or a related technical discipline, OR 3+ years of relevant work experience.
Preferred Qualification:
- 1–2 years of hands-on software engineering experience building, delivering, and supporting enterprise-grade Java/J2EE applications, preferably using Spring Boot, RESTful APIs, microservices, and modern front-end frameworks such as Angular or React.
- Hands-on experience with AI-assisted software delivery, including effective use of generative AI coding assistants and developer productivity tools such as Claude Code CLI, GitHub Copilot, Cline, or similar tools for prompt engineering, AI-supported code review, test generation, documentation, and productivity workflows.
- Working knowledge of applied AI concepts relevant to software engineering, such as large language models, embeddings, retrieval-augmented generation, agentic workflows, model limitations, data privacy, and responsible AI practices.