Pi Sulaiman

Key Skills

Software

CVAT

LabelImg

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Text

Video

Top Label Types

Bounding Box

Segmentation

Classification

Object Detection

Freelancer Overview

AI Data Specialist with experience working on large-scale datasets involving text, image, document, and structured data annotation. Skilled in classification, bounding box annotation, segmentation, object detection, and entity recognition (NER) while maintaining high accuracy and consistency across labeling tasks. Experienced in reviewing and organizing datasets, validating labeled data, and ensuring compliance with annotation guidelines for machine learning and AI model training. In addition to annotation work, I have experience supporting AI training workflows such as prompt-response evaluation, data categorization, dataset preparation, and AI output rating. I have worked on multiple data-driven projects requiring attention to detail, quality assurance, and large dataset management. My background in managing digital projects and collaborating with teams enables me to maintain efficiency, accuracy, and scalability in AI data labeling pipelines.

Labeling Experience

E-Commerce Product Image & Attribute Annotation for AI Search Optimization

CVATImageBounding BoxSegmentation

Worked on annotating large-scale e-commerce product datasets to improve AI-powered search, recommendation systems, and visual recognition models. The project involved: Drawing bounding boxes around products Multi-class classification (category, sub-category, material, style) Background segmentation for cleaner model training Attribute tagging (color, pattern, size, brand visibility) Annotated approximately 25,000+ images with strict quality control standards. Maintained labeling consistency using predefined taxonomies and validation checklists. Used Python scripts to automate label validation and ensure dataset integrity. Achieved 98%+ annotation accuracy through double-review QA processes and guideline adherence.

Worked on annotating large-scale e-commerce product datasets to improve AI-powered search, recommendation systems, and visual recognition models. The project involved: Drawing bounding boxes around products Multi-class classification (category, sub-category, material, style) Background segmentation for cleaner model training Attribute tagging (color, pattern, size, brand visibility) Annotated approximately 25,000+ images with strict quality control standards. Maintained labeling consistency using predefined taxonomies and validation checklists. Used Python scripts to automate label validation and ensure dataset integrity. Achieved 98%+ annotation accuracy through double-review QA processes and guideline adherence.

2025 - 2025

Education

Work History