Uchechukwu D.

Key Skills

Software

Labelbox

Prodigy

CVAT

Label Studio

Appen

Top Subject Matter

Healthcare – Medical Records & Patient Data

Finance – Risk Analysis & Fraud Detection

E-commerce – Product Categorization & Customer Support

Top Data Types

Text

Image

Top Task Types

Bounding Box

Classification

Object Detection

Freelancer Overview

I am a detail-oriented AI Data Annotator and AI Training Contributor with experience evaluating AI-generated content, performing data labeling tasks, and improving dataset quality through accurate annotation and validation. My work involves reviewing responses for factual accuracy, relevance, safety, and instruction adherence, while applying detailed guidelines to ensure consistency across large-scale datasets. I have developed strong skills in text classification, content evaluation, fact-checking, prompt engineering, and quality assurance, enabling me to deliver high-quality outputs with a high level of accuracy. In addition to data annotation, I have experience conducting research, verifying information from reliable sources, and providing structured feedback to improve AI model performance. My background has strengthened my analytical thinking, attention to detail, and ability to identify inconsistencies, labeling errors, and quality issues. I am proficient with tools such as Microsoft Excel, Google Sheets, ChatGPT, and other AI platforms, and I am committed to producing reliable training data that supports the development of accurate and effective AI systems.

Labeling Experience

Multimodal Data Labeling & AI Training Dataset Annotation

TextClassification

Worked on a large-scale AI training data annotation project focused on improving model understanding across text and image-based datasets. Responsibilities included labeling and classifying textual inputs for sentiment, intent, and topic relevance, as well as annotating images using bounding boxes for object detection tasks. The project involved thousands of mixed-domain samples, requiring strict adherence to labeling guidelines to ensure high-quality training data for machine learning models. Tasks were performed using internal annotation tools with a focus on consistency, precision, and contextual accuracy. Quality assurance measures included multi-pass review, cross-validation of labels, and correction of inconsistencies flagged during audit cycles. Maintained high annotation accuracy while meeting daily productivity targets in a fast-paced workflow environment.

2024 - Present

Education

Work History