Anieto A.

Key Skills

Software

Labelbox

CVAT

Top Subject Matter

AI training datasets for object detection and related supervised learning tasks.

Sentiment/intent classification and supervised learning dataset preparation.

Legal Services & Contract Review

Top Data Types

Image

Text

Document

Top Task Types

Object Detection

Bounding Box

Segmentation

Classification

Entity (NER) Classification

Freelancer Overview

PHE Tutor / Sports Coordinator in Contract Review, Compliance, and Legal Research. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox and CVAT. Education includes Bachelor’s Degree, Alex Ekwueme Federal University Alike Ndufu Ikwo (2021) and Master’s Class, N/A (2021). AI-training focus includes data types such as Image and Text and labeling workflows including Object Detection, Bounding Box, and Segmentation.

Labeling Experience

AI Data Annotation Contributor (Project-Based Work) | Remote | 2024 – Present

CVATTextEntity (NER) ClassificationBounding Box

Contributed to remote AI training dataset labeling tasks spanning sentiment analysis and intent classification. Structured and prepared labeled data for supervised machine learning training scenarios with consistent annotation standards. Performed bounding box annotation for object detection sample datasets to support multi-modality training needs. • Sentiment analysis and intent classification labeling • Image tagging label creation for supervised training datasets • Bounding box annotation for object detection sample datasets • Uniform labeling standards to maintain model training integrity

2024 - Present

Freelance Data Labeling Specialist (AI Training Support) | Remote | 2024 – Present

LabelboxImageObject DetectionBounding Box

Provided remote image annotation for AI model training tasks, including object detection labeling with consistent application of annotation guidelines. Reviewed outputs and corrected labeling errors to improve dataset accuracy and reduce error rates. Supported downstream AI training data preparation workflows for machine learning pipelines. • Bounding box-style object detection labeling • Validation and quality control (QA) of labeled outputs • Error detection and correction to improve dataset integrity • Efficiency-focused completion under strict deadlines

2024 - Present

Education

Work History