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AI Engineer with less than a year in Deep Learning & NLP
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Assessing your cultural and operational fit
Results-driven Artificial Intelligence (AI) Engineer with hands-on experience in designing, training, fine-tuning, and deploying deep learning systems across computer vision, Natural Language Processing (NLP), and Generative AI. Proficient in Python-based Machine Learning (ML) workflows using PyTorch, TensorFlow, Hugging Face, and Open Neural Network Exchange (ONNX). Focuses on transforming research prototypes into production-ready applications. Applies model optimization techniques, including Low-Rank Adaptation (LoRA) fine-tuning and ONNX inference acceleration, to deliver efficient, scalable solutions. Passionate about building impactful systems in healthcare AI and bilingual NLP.
Higher Technological Institute (HTI) 10th of Ramadan
Communications & Electronics Engineering · Communications & Electronics Engineering
August 1, 2023 – June 30, 2027
Digital Egypt Pioneers Initiative (DEPI)
Generative AI Trainee
November 1, 2025 – Present
Cairo, Cairo Governorate, Egypt
Parkinson Machine Model
June 24, 2026 – Present
Developed a machine learning model to assist in Parkinson's disease analysis and research.
View ProjectTone Controller AI (English-Arabic Style Rewriter)
June 24, 2026 – Present
Engineered a bilingual Natural Language Processing (NLP) tool designed to rewrite text styles across English and Arabic.
View ProjectResNet50 Image Classification
June 24, 2026 – Present
Implemented and trained a ResNet50 model for image classification tasks utilizing CIFAR-10 and CIFAR-100 datasets.
View ProjectCultural Fit Analysis
The candidate's personal projects, particularly the 'Tone Controller AI (English-Arabic Style Rewriter)' and 'Parkinson Machine Model', indicate an interest in diverse applications of AI, including bilingual NLP and healthcare. Their current internship as a Generative AI Trainee aligns well with the target role of an AI Engineer, showing a focused career path. The breadth of skills listed (Deep Learning, CNNs, NLP, Computer Vision, Generative AI, LLMs, RAG, Prompt Engineering) suggests adaptability and a willingness to explore various AI domains. However, the candidate is still an undergraduate, which might imply less real-world industry exposure beyond the internship.
Soft Skills & Operational Fit
The candidate demonstrates a proactive attitude towards learning and applying advanced AI concepts, as evidenced by their internship and personal projects. Their collaboration in team-based projects suggests an ability to work within industry-standard software development practices. However, without specific psychometric test results, a detailed assessment of logical reasoning, work attitude, stress handling, and team collaboration cannot be fully provided.