Ahmed S.

Key Skills

Software

Label Studio

CVAT

Roboflow

Argilla

Top Subject Matter

Generative AI/Natural Language Processing

Image Classification/Computer Vision

Top Data Types

Text

Image

Document

Top Task Types

Prompt + Response Writing (SFT)

Classification

Freelancer Overview

I am an AI and Machine Learning specialist with practical experience in data preparation, model fine-tuning, and AI evaluation across both Computer Vision and Natural Language Processing domains. Alongside pursuing concurrent degrees in Artificial Intelligence and Computer Science, I have developed a strong technical foundation in handling complex datasets, ensuring they are optimized for high-performing neural networks and Large Language Models (LLMs). My hands-on experience ranges from detailed image processing and semantic segmentation for medical diagnostic systems to prompt engineering and Supervised Fine-Tuning (SFT) for generative models. As a scholar in the Generative AI & Data Science track (DEPI), I have honed my ability to evaluate multi-agent systems and craft high-quality instruction-response pairs, allowing me to consistently deliver precise, accurate, and scalable training data for advanced AI architectures.

Labeling Experience

Generative AI & Data Science Scholar - Prompt Engineering and LLM Interaction

TextPrompt + Response Writing (SFT)

As a Generative AI & Data Science Scholar at the Digital Egypt Pioneers Initiative (DEPI), I engaged in prompt engineering and interactions with large language models (LLMs). My responsibilities included designing effective prompts, evaluating model responses, and optimizing output for generative tasks. I also implemented attention-based NLP approaches for data processing and model improvement. • Developed and refined prompts to direct model outputs for various NLP tasks. • Contributed to full-stack AI projects that required effective interaction with LLMs and generative AI systems. • Utilized Hugging Face libraries for model integration and experimentation. • Focused on real-world applications of prompt engineering in generative AI scenarios.

2025 - Present

Machine Learning Intern - MNIST Image Labeling and Classification

ImageClassification

During my Machine Learning Internship at NTI, I trained and validated models on labeled image datasets, including the MNIST handwritten digit dataset. Tasks included organizing, preprocessing, and using labeled data to optimize model accuracy. The experience strengthened my practical skills in handling, annotating, and utilizing image data for machine learning pipelines. • Prepared datasets and ensured high label quality before model training. • Performed data validation and post-labeling evaluation for accuracy. • Focused on digit classification within a supervised learning framework. • Applied deep learning (CNN) and Scikit-learn workflows on annotated images.

2025 - 2025

Education

Work History