Ahmad Shallal

Key Skills

Software

Appen

Clickworker

CrowdSource

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Bounding Box

Classification

Data Collection

Object Detection

Freelancer Overview

As a data labeling expert with one year of hands-on experience, I’ve worked extensively on preparing high-quality training data for machine learning and AI models across diverse domains including computer vision, natural language processing (NLP), and speech recognition. My core responsibilities involved annotating images, text, and audio using tools such as Labelbox, CVAT, and Prodigy, while following strict project guidelines and quality assurance protocols. I’ve consistently maintained accuracy rates above 98% through a strong attention to detail and a deep understanding of annotation taxonomies and edge cases. What sets me apart is my ability to quickly adapt to new labeling tools and project requirements, and to collaborate effectively with data scientists and ML engineers to refine annotation standards for improved model performance. I’ve contributed to projects involving sentiment analysis, named entity recognition (NER), object detection, and image segmentation. My work has directly supported the development of high-performing AI systems by ensuring the training data is clean, consistent, and contextually accurate.

Labeling Experience

High-Accuracy Image Annotation for Autonomous Vehicles

ClickworkerImageBounding Box

I contributed to a large-scale computer vision project aimed at improving object detection models for autonomous driving systems. My role involved annotating thousands of images with bounding boxes and segmentation masks for vehicles, pedestrians, traffic signs, and road markings. I adhered to strict labeling guidelines and handled edge cases such as occlusions, overlapping objects, and low-light environments. To ensure high data quality, I performed regular quality checks, used Python scripts to flag anomalies, and participated in weekly review sessions with the ML team. This project helped the client increase their model’s mAP (mean Average Precision) by over 12%, significantly enhancing detection accuracy in real-world scenarios.

I contributed to a large-scale computer vision project aimed at improving object detection models for autonomous driving systems. My role involved annotating thousands of images with bounding boxes and segmentation masks for vehicles, pedestrians, traffic signs, and road markings. I adhered to strict labeling guidelines and handled edge cases such as occlusions, overlapping objects, and low-light environments. To ensure high data quality, I performed regular quality checks, used Python scripts to flag anomalies, and participated in weekly review sessions with the ML team. This project helped the client increase their model’s mAP (mean Average Precision) by over 12%, significantly enhancing detection accuracy in real-world scenarios.

2023

Education

Work History