Mohamed Almorshdy

Key Skills

Software

AWS SageMaker

CrowdFlower

CrowdSource

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Image

Text

Video

Top Label Types

Action Recognition

Audio Recording

Classification

Computer Programming Coding

Data Collection

Emotion Recognition

Text Generation

Text Summarization

Freelancer Overview

I am an AI and Machine Learning Engineer with hands-on experience in building and deploying end-to-end machine learning solutions, particularly in domains such as healthcare, finance, and personalized learning. My expertise includes data preparation, feature engineering, and model evaluation, with a strong focus on ensuring high-quality, well-annotated datasets that drive robust AI models. I am proficient in Python, SQL, and widely used ML libraries like Pandas, NumPy, and Scikit-learn, and have worked extensively with NLP, LLMs, and retrieval-augmented generation systems. Through projects like breast cancer prediction and credit risk assessment, I have developed skills in cleaning, structuring, and validating data, as well as designing systems that rely on accurate and consistent training data for real-world impact. I am passionate about leveraging my technical background to support AI training data initiatives that require meticulous attention to data quality and annotation standards.

Labeling Experience

AI Engineer

CrowdsourceImageEmotion Recognition

I contributed to a large-scale image annotation project focused on facial emotion recognition to support the development of computer vision and deep learning models. Using the CrowdSource labeling platform, I accurately labeled facial images into predefined emotion categories such as happy, sad, angry, surprised, neutral, and other expressions, strictly following detailed annotation guidelines and project standards. My role involved high-precision labeling, reviewing complex or ambiguous cases, and ensuring consistency and reliability across thousands of image samples. I applied rigorous quality control practices, including self-verification, guideline compliance, and consistency checks to maintain high annotation accuracy and dataset integrity. I also demonstrated strong attention to detail and efficiency while working under defined productivity and quality benchmarks.

I contributed to a large-scale image annotation project focused on facial emotion recognition to support the development of computer vision and deep learning models. Using the CrowdSource labeling platform, I accurately labeled facial images into predefined emotion categories such as happy, sad, angry, surprised, neutral, and other expressions, strictly following detailed annotation guidelines and project standards. My role involved high-precision labeling, reviewing complex or ambiguous cases, and ensuring consistency and reliability across thousands of image samples. I applied rigorous quality control practices, including self-verification, guideline compliance, and consistency checks to maintain high annotation accuracy and dataset integrity. I also demonstrated strong attention to detail and efficiency while working under defined productivity and quality benchmarks.

2023 - 2025

Education

Work History