Amr Emam Email and Phone Number

• Contributed to the development and enhancement of Enspct (formerly Bravo), a field inspection system, leveraging my skills in .NET and Angular.• Designed and developed new features for Enspct, leveraging .NET and Angular to meet client requirements and enhance product functionality.• Customized existing features of Enspct to align with client needs, ensuring seamless integration and satisfaction.• Successfully completed training projects and tasks in OutSystems, demonstrating proficiency in the platform's low-code development environment.• Acquired certification in OutSystems, solidifying expertise in utilizing its features and functionalities for software development.

• Implemented and deployed OCR Post correction model for Arabic language- Designed and developed an OCR post-correction model using a transformer-based architecture to enhance the accuracy of OCR (Optical Character Recognition) output.- Trained the model on a dataset of 5M+ sentences obtained from various news websites, demonstrating proficiency in data acquisition and data preprocessing techniques. - Developed a Noise Adder module to simulate OCR errors on the clean dataset, enabling the model to learn how to correct common OCR errors and improve the overall accuracy of the OCR output- Utilized state-of-the-art natural language processing (NLP) techniques to enhance the model's performance, including tokenization, normalization, and language modeling.• Implemented and deployed text errors classification model for Arabic language- Developed Noise adder module to create dataset with 4 classes of errors (spelling error, grammar error, context error, segmentation error) - Fine-tuned AraBERT model with the created dataset which resulted +90% accuracy on test data• Use MPI with HPC system to parallelize the training and inference of AI models

• Implemented Motion-in-between project (Graduation Project)- This project aims to interpolate the motion between keyframes using RNN (Demo: https://drive.google.com/file/d/1MmRpG0jgRxaEr-39zum_92DdlpHvJtJm/view?fbclid=IwAR1hMu_B-qH6aZunMlwAoi-hf9T2HIdJ7MN6TJFZYVt5oFIcuj61py3byDY)- Demonstrated proficiency in using RNN algorithms, deep learning frameworks, and other relevant machine learning tools and techniques.- Trained the model on the LAFAN1 dataset using an AWS EC2 instance- Deployed the model with API using FastAPI, a modern and efficient web framework for building APIs in Python, enabling seamless integration of the model with other applications or systems.• Implemented Wuzzuf Data analysis project- Conducted data analysis on the Wuzzuf dataset, which involved cleaning, transforming, and exploring the data to extract meaningful insights and statistics- Applied the K-means clustering algorithm to group jobs and companies based on similar characteristics, providing a deeper understanding of the job market and industry trends- Utilized Java Spark for exploratory data analysis (EDA), leveraging its distributed computing capabilities to handle large volumes of data efficiently.- Utilized Java XChart for data visualization, creating clear and informative charts and graphs to communicate the findings of the analysis effectively.- Developed a Spring Boot API to deploy the model, allowing for easy access and integration of the analysis results in other applications or systems.

• Designed and developed mobile applications using Java and Flutter• Developed Fleet Management project to manage EVA Pharma’s fleets• Accomplished First place over ten teams in EVA’s project competition• Testing and debugging the application to ensure optimal performance