Review and validate annotated datasets for accuracy, completeness, and adherence to project labeling standards. Provide feedback to annotators, identify and resolve data inconsistencies, and collaborate with project teams to improve dataset quality for AI applications.

Design, build, and maintain data collection, cleaning, transformation, validation, and storage pipelines. Collaborate with annotation, QA, and AI teams to prepare high-quality datasets for model training and automate dataset validation and curation workflows.

Lead multiple AI data labeling projects end-to-end, coordinate annotators, QA reviewers, and clients, monitor timelines and resources, ensure high-quality dataset delivery, and implement process improvements to increase operational efficiency.