Oluwatomilola Moses

My experience covers multimodal data annotation and AI evaluation across image, video, audio, and text. I’ve worked with tasks such as bounding boxes, polygons, semantic and instance annotation, classification, intent and entity labeling, transcription, content review, and annotation quality checks. This also includes QA/QC, annotation validation, error identification, guideline adherence, LLM response evaluation, NLP, computer vision, and Human-in-the-Loop workflows. I’m comfortable working with Label Studio and CVAT, alongside AI-assisted review tools, and bring strong visual, analytical, and detail-oriented judgment to AI data work. With my hands-on experience in prompt writing, response ranking, comparative analysis and speech to text alignment, I have been able to see how much AI depends on human judgment, from building models to reinforcement learning, where they learn from people over time. As model development evolve, there's also a rising need for AI Models that are accurate, precise and reliable across every task within their domain expertise, and it takes human input to complement and strengthen high fidelity model development. I'm always eager to contribute clear, precise and consistent inputs according to given project guidelines and my background in multimodal annotation and quality management systems gives me solid grounding in why we do the work, how to do it, and the workflow that ensures quality.