Marvelous T.

Key Skills

Software

AWS SageMaker

Top Subject Matter

Computer Vision (Object Detection and Semantic Segmentation)

Multi-modal AI Training Data (Image, Text, Sensor)

Top Data Types

Image

Text

Audio

Top Task Types

Segmentation

Entity (NER) Classification

Question Answering

Freelancer Overview

Data Labeler | Atlas Capture (Remote). Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes O Level Certificate, Federal University of Technology Owerri (2026). AI-training focus includes data types such as Image and Text and labeling workflows including Segmentation and Entity (NER) Classification.

Labeling Experience

Data Labeler | Atlas Capture (Remote)

ImageSegmentation

Annotated and segmented large-scale image datasets for computer vision, focusing on pixel-level accuracy. Collaborated with AI researchers to validate labeling guidelines and reduce annotation error rates. Conducted quality control (QC) checks to maintain data integrity and consistency across the dataset.• Pixel-perfect object detection and semantic segmentation labeling.• Validation and refinement of labeling guidelines with researchers.• Quality control feedback loops to maintain integrity standards.• Dataset labeling for large image collections supporting ML training.

2024 - Present

Data Annotation Team Lead (Co-founder Group) | Zora AI Development Project

TextEntity (NER) Classification

Led an annotation team for core datasets that powered the development of an AI system. Developed and documented rigorous labeling taxonomies for multi-modal data involving image, text, and sensor-level inputs. Implemented workflow management strategies to increase annotation throughput without compromising quality.• Leadership of an annotation group for training datasets.• Creation of multi-modal labeling taxonomies (image, text, sensor).• Workflow management to increase throughput by 35%.• Coordination to ensure consistent, high-quality annotations.

2023 - 2024

Education

Work History