Gary Gatab

Key Skills

Software

Scale AI

Labelbox

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Text

Image

Top Label Types

Entity Ner Classification

Classification

Text Summarization

Bounding Box

Segmentation

Freelancer Overview

I am an experienced data trainer and application system engineer with a strong background in preparing, labeling, and reviewing datasets for AI and machine learning projects. My hands-on experience includes using tools like SuperAnnotate, Labelbox, and DEEL to annotate text, image, audio, and video data, ensuring high accuracy and consistency according to detailed quality standards. I have a solid foundation in Python, Django, SQL, and cloud platforms like Azure, which enables me to understand the technical requirements of AI systems and contribute to improving model performance. My work spans multiple domains, including medical data and customer support, and I am passionate about building reliable training data pipelines, identifying errors, and collaborating with teams to deliver scalable AI solutions. I am committed to continuous learning and always strive to meet productivity and accuracy targets in fast-paced environments.

Labeling Experience

Data Trainer (SME, SuperAnnotate, Freelance)

SuperannotateImageClassification

As a Data Trainer at SuperAnnotate, I was responsible for preparing, labeling, reviewing, and improving datasets to train AI and machine learning models. My core duty was to guarantee data accuracy, structure, and alignment with rigorous quality standards to optimize AI learning. I consistently contributed feedback to enhance overall AI model performance. • Labeled and annotated text, images, audio, or video data following explicit guidelines. • Reviewed, corrected, and validated data for accuracy and consistency. • Provided suggestions for quality improvements and performed QA tasks. • Ensured achievement of productivity and accuracy targets within project deadlines.

As a Data Trainer at SuperAnnotate, I was responsible for preparing, labeling, reviewing, and improving datasets to train AI and machine learning models. My core duty was to guarantee data accuracy, structure, and alignment with rigorous quality standards to optimize AI learning. I consistently contributed feedback to enhance overall AI model performance. • Labeled and annotated text, images, audio, or video data following explicit guidelines. • Reviewed, corrected, and validated data for accuracy and consistency. • Provided suggestions for quality improvements and performed QA tasks. • Ensured achievement of productivity and accuracy targets within project deadlines.

2025

Image & Text Annotation for AI-Powered Document Processing

LabelboxImageBounding BoxEntity Ner Classification

As part of Fujitsu’s Application System Engineering team, I contributed to an AI training initiative focused on automating document processing for enterprise clients. Responsibilities included labeling scanned documents and form images with bounding boxes and segmentation to identify tables, fields, and handwritten entries. Additionally, I performed entity tagging (NER) and classification of extracted text to support Natural Language Processing (NLP) models. The project involved annotating 10,000+ documents across multiple formats while ensuring a 95%+ accuracy rate through peer reviews and iterative quality checks. My software engineering background enabled me to work closely with developers to integrate annotated datasets into the machine learning pipeline, optimizing data flow and improving model training efficiency.

As part of Fujitsu’s Application System Engineering team, I contributed to an AI training initiative focused on automating document processing for enterprise clients. Responsibilities included labeling scanned documents and form images with bounding boxes and segmentation to identify tables, fields, and handwritten entries. Additionally, I performed entity tagging (NER) and classification of extracted text to support Natural Language Processing (NLP) models. The project involved annotating 10,000+ documents across multiple formats while ensuring a 95%+ accuracy rate through peer reviews and iterative quality checks. My software engineering background enabled me to work closely with developers to integrate annotated datasets into the machine learning pipeline, optimizing data flow and improving model training efficiency.

2024 - 2025

Customer Feedback Text Annotation for NLP Model Training

Scale AITextEntity Ner ClassificationClassification

Worked on a large-scale Natural Language Processing (NLP) project aimed at improving customer support automation. Annotated thousands of text entries from customer service logs, emails, and feedback forms. Tasks included intent classification, sentiment/emotion tagging, and entity recognition (e.g., product names, locations, issue types). Also contributed to text summarization datasets to support response generation models. Maintained a 96% accuracy rate through double-blind quality checks and weekly review sessions.

Worked on a large-scale Natural Language Processing (NLP) project aimed at improving customer support automation. Annotated thousands of text entries from customer service logs, emails, and feedback forms. Tasks included intent classification, sentiment/emotion tagging, and entity recognition (e.g., product names, locations, issue types). Also contributed to text summarization datasets to support response generation models. Maintained a 96% accuracy rate through double-blind quality checks and weekly review sessions.

2023 - 2023

Education

Work History