Kenechukwu O.

Key Skills

Software

CVAT

Labelbox

V7 Labs

Top Subject Matter

Automotive (Autonomous Driving)

Technology (AI & Machine Learning)

Transportation

Top Data Types

Image

Video

Text

Top Task Types

Polygon

Segmentation

Freelancer Overview

With over three years of hands-on experience in data labeling and AI training data preparation, I have developed high-quality annotated datasets that power computer vision and natural language processing models. In my previous roles as a Data Annotation Specialist and AI Training Data Associate, I worked on large-scale projects involving image segmentation, object detection, bounding box annotation, semantic segmentation, and text classification. I consistently maintained annotation accuracy above 95% while contributing to the creation and refinement of labeling guidelines to improve team efficiency and data consistency. I am proficient with industry-standard tools such as LabelStudio, CVAT, Supervisely, and Amazon SageMaker Ground Truth. My key strengths include attention to detail, strong understanding of quality assurance processes, and the ability to handle complex multi-class labeling tasks across diverse domains including autonomous driving, medical imaging, and e-commerce product recognition. What sets me apart is my experience leading small annotation teams, implementing inter-annotator agreement checks, and providing feedback loops that improved overall dataset quality and model performance. I am passionate about delivering clean, reliable training data that accelerates AI development.

Labeling Experience

Autonomous Vehicle Dataset Annotation for Object Detection & Segmentation

ImageBounding Box

Led a team of 8 annotators in the creation of a high-quality training dataset comprising 85,000+ images for an autonomous vehicle perception system. Performed precise bounding box, polygon, and semantic segmentation annotations across 18 object classes including vehicles, pedestrians, cyclists, traffic signs, traffic lights, road markings, and obstacles under diverse conditions (day/night, weather variations, urban/highway scenarios).

2023 - 2024

Education

Work History