Chukwuemeka Nkwocha

I am a detail-oriented Video Data Reviewer and AI Data Annotation Specialist with a proven track record in reviewing, labeling, and refining video, audio, and text datasets for AI training. My expertise lies in frame-by-frame video analysis, action segmentation, and annotation quality control, consistently achieving 98%+ accuracy. I am adept at identifying hand movements, object interactions, and temporal boundaries, ensuring strict adherence to annotation guidelines. I excel at delivering consistent, high-quality output in repetitive workflows and providing actionable feedback to improve model performance. My experience includes recording and reviewing high-quality first-person (egocentric) videos, analyzing detailed hand-object interactions, and producing structured datasets for computer vision and robotics AI models. I have evaluated and refined AI-generated outputs, applying RLHF techniques to enhance their accuracy, relevance, and consistency. I am also skilled in annotating short video clips, performing precise frame-by-frame analysis, and verifying left vs. right hand usage across large datasets. My technical toolkit includes CVAT, Labelbox, Encord, V7, and Scale. I possess native-level English proficiency, can work independently in remote settings, and maintain strong focus and endurance for long-duration video review tasks.