Unogu Chukwuemeka

Key Skills

Software

Scale AI

Telus

Lionbridge

Top Subject Matter

No subject matter listed

Top Data Types

Video

Image

Top Label Types

Segmentation

Freelancer Overview

I am a detail-oriented Data Annotation and Machine Learning Support Specialist with hands-on experience delivering high-quality labeled datasets across image, video, text, and audio domains. My background includes working with leading platforms such as Atlas Capture, Lionbridge (TELUS AI), and Appen, where I have annotated data for computer vision and natural language processing projects, including tasks like bounding boxes, segmentation, NER, sentiment analysis, and speech-to-text. I am skilled in using annotation tools, Excel, Google Sheets, CSV, and JSON formats, and I consistently follow complex guidelines to meet strict quality and productivity standards. My commitment to accuracy and my ability to conduct quality reviews have helped ensure reliable data for AI model training at production scale.

Labeling Experience

Data Labelling

Scale AIVideoSegmentation

Project: (Video Data Labelling) The project is a large-scale video annotation project, focused on preparing high-quality training data for computer vision and machine learning models. The primary objective of the project was to accurately label and structure video data to improve model performance in object detection, tracking, and activity recognition tasks. Scope of the Project The project involved annotating and reviewing video datasets across multiple categories. This included frame-by-frame analysis of videos to identify, classify, and track specific objects and events according to detailed client guidelines. The work required strong attention to detail, consistency in annotation standards, and adherence to strict quality benchmarks.

Project: (Video Data Labelling) The project is a large-scale video annotation project, focused on preparing high-quality training data for computer vision and machine learning models. The primary objective of the project was to accurately label and structure video data to improve model performance in object detection, tracking, and activity recognition tasks. Scope of the Project The project involved annotating and reviewing video datasets across multiple categories. This included frame-by-frame analysis of videos to identify, classify, and track specific objects and events according to detailed client guidelines. The work required strong attention to detail, consistency in annotation standards, and adherence to strict quality benchmarks.

2023 - 2025

Data Annotator

LionbridgeImageSegmentation

The project was a high-precision data annotation across multiple modalities which includes quality reviews and audits to ensure dataset reliability.

The project was a high-precision data annotation across multiple modalities which includes quality reviews and audits to ensure dataset reliability.

2022 - 2023

Data Annonator

TelusImageSegmentation

The project is about AI model training through accurate labeling and validation of datasets.

The project is about AI model training through accurate labeling and validation of datasets.

2022 - 2023

Education

Work History