AI Trainer
AI Training Project – Image Annotation & Visual Data Optimization (with Invisible Technologies) Contributed to a large-scale AI training initiative focused on improving computer vision model performance through high-quality image annotation and evaluation. The project involved working with diverse photographic datasets to support supervised learning pipelines. Key responsibilities included: Performing detailed image annotation and labeling (objects, boundaries, contextual elements) following strict guideline protocols. Conducting quality assurance (QA) to ensure dataset accuracy, consistency, and bias minimization. Interpreting complex visual scenes and applying semantic tagging to enhance model understanding. Collaborating with distributed teams to refine annotation standards and improve workflow efficiency. Providing feedback on edge cases to support model training optimization and dataset refinement. Impact: Helped improve training dataset reliability, directly contributing to enhanced model accuracy in object detection and classification tasks. Maintained high precision and consistency under production-level throughput requirements.