Eyad Gad

Key Skills

Software

Appen

CVAT

Datasaur

Img Lab

Labelbox

Label Studio

Roboflow

SuperAnnotate

Top Subject Matter

Medical Imaging and Healthcare AI Data Labeling

IoT Network Security and Anomaly Detection

Natural Language Processing for Text Analysis

Top Data Types

Image

Text

Video

Top Task Types

Classification

Computer Programming Coding

Data Collection

Fine Tuning

Segmentation

Freelancer Overview

I have extensive experience in data labeling and AI training data preparation, particularly in computer vision and natural language processing. My work includes annotating large datasets for deep learning models, focusing on tasks like object detection, segmentation, and classification. I have contributed to projects involving medical imaging, IoT intrusion detection, and real-time video analysis, ensuring high-quality annotations that drive model accuracy and performance. My expertise extends to developing custom annotation pipelines and optimizing workflows to handle large-scale datasets efficiently. With a strong foundation in Python, I have automated repetitive labeling tasks and validated data integrity. My technical skills, coupled with my attention to detail and ability to manage complex datasets, make me a reliable contributor to AI training data projects.

Labeling Experience

Text Data Annotation for NLP Model Fine-Tuning

LabelboxTextEntity Ner ClassificationText Generation

In this project, I performed text data labeling to support LLM fine-tuning. My tasks included labeling text for Named Entity Recognition (NER), classification, and creating prompt-response pairs for supervised fine-tuning. I ensured consistency, accuracy, and adherence to guidelines to maintain high data quality. The project involved thousands of text entries, and I collaborated with team members to meet quality control standards and improve model performance.

In this project, I performed text data labeling to support LLM fine-tuning. My tasks included labeling text for Named Entity Recognition (NER), classification, and creating prompt-response pairs for supervised fine-tuning. I ensured consistency, accuracy, and adherence to guidelines to maintain high data quality. The project involved thousands of text entries, and I collaborated with team members to meet quality control standards and improve model performance.

2023 - 2023

Medical Imaging Annotation for AI Models

RoboflowImageBounding BoxSegmentation

Annotated large-scale medical imaging datasets, including MRI and ultrasound images, for tasks such as tumor segmentation, disease classification, and object detection. Utilized tools like Roboflow, LabelBox, and Appen to ensure accurate pixel-level segmentation and bounding box annotations. The project involved labeling over 5000 medical images to train and validate deep learning models for cancer detection and segmentation. Maintained strict quality control measures, including inter-annotator agreement checks and iterative feedback, to ensure high-quality annotations that met clinical research standards.

Annotated large-scale medical imaging datasets, including MRI and ultrasound images, for tasks such as tumor segmentation, disease classification, and object detection. Utilized tools like Roboflow, LabelBox, and Appen to ensure accurate pixel-level segmentation and bounding box annotations. The project involved labeling over 5000 medical images to train and validate deep learning models for cancer detection and segmentation. Maintained strict quality control measures, including inter-annotator agreement checks and iterative feedback, to ensure high-quality annotations that met clinical research standards.

2022 - 2023

Education

Work History