Followed established protocols to assess and correct AI outputs, ensuring adherence to project-specific guidelines. Tasks involved maintaining high-quality standards while executing repetitive and complex annotation work, providing feedback to improve AI performance over time.

Executed tasks such as entity tagging, video annotation, and image-based recognition (e.g., identifying times on analogue clocks). Evaluated AI-generated textual outputs for comprehension and accuracy, ensuring all data met project criteria and quality standards.

Through careful evaluation and correction of AI outputs, models were refined for greater accuracy and reliability. Contributions supported improvements in the AI’s ability to interpret visual and textual information across general and domain-specific tasks.

• AI output evaluation and quality assurance

• Entity tagging, video and image annotation

• English comprehension and textual evaluation

• Domain-specific content validation

• Attention to detail, critical analysis, and iterative feedback

• Familiarity with AI training platforms and annotation tools

• Efficient use of R & MatLab (programming languages)