Olutaiwo A.

AI models are only as good as the human feedback behind them. A response can look convincing and still contain factual errors, poor reasoning, inconsistent labels, or subtle quality issues that affect the final model. That’s where I come in. I work with AI teams to evaluate, annotate, review, and improve training data. My experience spans LLM evaluation, quality auditing, image and video annotation, multimodal data, response evaluation, and detailed QA work where consistency and guideline adherence matter. I don’t just complete tasks quickly. I pay attention to the edge cases—the ambiguous annotation, the hallucinated claim, the inconsistent label, the poorly grounded caption, or the model response that sounds right but fails the rubric. What I can help you with: • AI/LLM Evaluation – response scoring, ranking, factuality, relevance, reasoning, hallucination detection, and rubric-based evaluation • Data Annotation – image, video, text, document, and multimodal annotation • Quality Assurance – reviewing annotations, identifying inconsistencies, correcting errors, and maintaining dataset quality • Image & Video Annotation – bounding boxes, classification, captioning, object identification, and detailed visual evaluation • AI Training Data – creating, reviewing, and refining high-quality human feedback for machine learning systems • Data & Technical Tasks – Python, SQL, structured data review, and related analytical work I’m comfortable working with detailed guidelines, large task volumes, evolving rubrics, and projects where accuracy needs to remain consistent from the first task to the last. If you need someone who can look beyond “good enough,” catch the details others miss, and deliver reliable human judgment for your AI project, let’s talk.