Image-Based Q&A Quality Assurance & AI Training Data Review
Contributed to large-scale AI training projects aimed at enhancing multimodal and language model performance through precise data labeling and quality validation. The work involved reviewing pre-labeled image-based question-and-answer pairs, verifying classification accuracy, textual correctness, and compliance with strict annotation guidelines before integration into training pipelines. Key responsibilities included correcting mislabels, extracting exact text from images, rewriting responses for grammatical accuracy and standalone clarity, evaluating AI outputs with RLHF-aligned rubrics, flagging non-answerable items, and documenting edge cases. Maintained a consistent production rate of 7–8 minutes per item, processing thousands of entries across diverse image categories while ensuring high-quality, reliable datasets for enterprise AI model refinement.