Herbert Nwadike

Key Skills

Software

CVAT

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Freelancer Overview

I am a detail-oriented AI Data Annotation Specialist with hands-on experience in annotating, labeling, and validating diverse datasets for machine learning applications. My work spans text, image, and document-based data, where I excel at applying precise guidelines and maintaining high standards of accuracy and consistency. I have contributed to global remote projects with TELUS International AI and Mindrift, focusing on quality review, dataset curation, and supporting continuous improvement in AI training workflows. My skills include data annotation, quality control, and data validation, and I am proficient in using AI annotation platforms, browser-based tools, Excel, Google Sheets, and cloud-based workflows. I am committed to delivering reliable, high-quality data that powers effective AI solutions.

Labeling Experience

AI Data Annotator

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Contributed to AI training initiatives by annotating and validating text, image, and document datasets according to structured project guidelines. Responsibilities included tagging entities, classifying content, reviewing labeled data for accuracy and consistency, and identifying edge cases or inconsistencies that could impact model performance. Also participated in AI output evaluation tasks, comparing and scoring model-generated responses using defined criteria such as clarity, accuracy, contextual relevance, and tone. Supported dataset curation efforts by detecting duplicates, noise, anomalies, and potential labeling errors prior to final approval for model training.

Contributed to AI training initiatives by annotating and validating text, image, and document datasets according to structured project guidelines. Responsibilities included tagging entities, classifying content, reviewing labeled data for accuracy and consistency, and identifying edge cases or inconsistencies that could impact model performance. Also participated in AI output evaluation tasks, comparing and scoring model-generated responses using defined criteria such as clarity, accuracy, contextual relevance, and tone. Supported dataset curation efforts by detecting duplicates, noise, anomalies, and potential labeling errors prior to final approval for model training.

2022

Education

Work History