AI Training Data Annotation – Java/Spring Boot & Full Stack Code Review
Annotated and reviewed programming-related datasets used for AI model training and evaluation. The work focused on Java, Spring Boot, REST APIs, microservices, SQL/PostgreSQL, Angular, TypeScript, cloud deployment, and production debugging scenarios. Tasks included labeling code quality, identifying bugs, validating expected outputs, reviewing API logic, classifying technical issues, improving prompt/response quality, and checking whether AI-generated code followed clean architecture, security, testing, and maintainability standards. I also used my real production experience with financial and insurance platforms to evaluate answers involving backend systems, distributed services, authentication, messaging with Kafka/RabbitMQ, CI/CD, observability, and incident response.