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AI Engineer with less than a year in Machine Learning & LLM-based Applications
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Assessing your cultural and operational fit
AI Engineer and Data Science enthusiast with experience in developing machine learning and AI-driven systems, including predictive analytics, multimodal AI, and LLM-based applications. Skilled in data analysis, visualization, feature engineering, model optimization, and deploying scalable AI solutions using Python and modern ML frameworks. Interested in building efficient and reliable data-driven systems for real-world applications.
Cairo University
Bachelor's Degree · Computing and Artificial Intelligence
September 1, 2022 – Present
Datalentech
AI Engineer Intern
March 1, 2026 – May 31, 2026
Cairo, Cairo Governorate, Egypt
Tips Hindawi
Generative AI Program Trainee
August 1, 2025 – September 30, 2025
India
Ministry of Communications & Information Technology (DPEI Initiative)
IBM Data Scientist Track Intern
October 1, 2024 – May 31, 2025
Cairo, Cairo Governorate, Egypt
Cerebra Guard
June 26, 2026 – Present
• Developed an AI-powered stroke prediction system using cardiovascular datasets. • Trained and evaluated machine learning models for stroke prediction, achieving 98% accuracy using Random Forest. • Applied feature selection and class imbalance handling techniques to improve prediction performance on physiological signals. • Evaluated model performance using ROC-AUC, precision-recall, and other classification metrics. • Integrated the predictive model into a mobile application for continuous monitoring and real-time health alerts.
Personal AI Productivity Coach
June 26, 2026 – Present
• Developed an AI-powered productivity coach using Mistral with prompt engineering and RAG pipelines built using LangChain and ChromaDB. • Applied model quantization and optimization techniques to run LLMs efficiently on limited resources. • Built automated productivity scoring, habit tracking, and daily reporting workflows. • Used ngrok for secure deployment and real-time testing of AI services.
View ProjectPredictive-Maintenance (Industrial Equipment)
June 26, 2026 – Present
• Developed an end-to-end predictive maintenance system to estimate the remaining useful life (RUL) of industrial equipment using NASA's C-MAPSS dataset. • Performed data preprocessing, feature engineering, and exploratory data analysis to identify sensor degradation patterns and improve model performance. • Trained and evaluated machine learning and deep learning models, including Random Forest, XGBoost, CatBoost, and LSTM for RUL prediction. • Applied hyperparameter tuning and evaluated model performance using RMSE and RUL-specific scoring metrics.
Nasaq (Graduation Project)
March 1, 2026 – Present
• Fine-tuned Gemma 3-4B Vision-Language Model using LoRA and SFT for Arabic calligraphy transcription and script classification (Naskh, Thuluth, Diwani, Kufic, Muhaqqaq). • Achieved a 96.43% calligraphy classification accuracy and an 86.93% Levenshtein Ratio, reducing Character Error Rate (CER) to 27.60% and outperforming baseline models. • Designed system architecture, including layout analysis, user feedback loop, and historical text retrieval for cultural heritage preservation.
Presentation Agent
March 1, 2026 – Present
• Built Q&A flow integrating intent classification, vector retrieval (ChromaDB), and LLM generation. • Implemented REST and WebSocket APIs with FastAPI for real-time and synchronous interactions. • Integrated Google Gemini to generate dynamic responses and audio scripts.
View ProjectTelebot- Telemarketing System
March 1, 2026 – Present
• Designed a turn-based call analysis pipeline, split the call into turns, and analyzed independently.
Cultural Fit Analysis
The candidate's diverse range of projects, from academic research (Nasaq) to personal initiatives (Presentation Agent, Cerebra Guard, Personal AI Productivity Coach), indicates a strong passion for AI and continuous learning. Their internships align well with an AI Engineer role, focusing on generative AI, call analysis, and data science workflows. The breadth of skills across AI concepts, frameworks, backend/APIs, and data handling suggests adaptability and a broad interest in the field, which is beneficial for cultural fit in an innovative AI team. The academic background in Computing and Artificial Intelligence further strengthens this alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven individual, undertaking multiple personal projects. Their involvement in student activities as an AI instructor suggests leadership potential and a willingness to share knowledge. The internships demonstrate an ability to work in structured environments and contribute to team projects. However, without direct interview data, it's difficult to assess communication style, problem-solving under pressure, or specific team collaboration dynamics.