Help & Supportcareers@gravityer.com
AI Engineer with 1+ years in Machine Learning & Python
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Recent Computer Engineering graduate passionate about Machine Learning and Artificial Intelligence, eager to begin my career and contribute to impactful, real-world projects. Skilled in Python, Scikit-learn, and TensorFlow through academic projects, internships, and hands-on AI evaluation work. Known for being a fast learner with strong problem-solving skills, a curious mindset, and a genuine drive to grow alongside experienced teams. Highly motivated to take my first professional steps and deliver real value from day one.
Modern Academy for Engineering and Technology
Bachelor of Engineering (B.Eng.) · Computer Engineering
January 1, 2020 – December 31, 2025
G2i
AI Training & Evaluation Engineer
February 1, 2025 – December 31, 2025
New York City, New York, United States
Digital Egypt Pioneers Initiative (DEPI – MCIT)
Machine Learning Engineer Intern
April 1, 2024 – November 30, 2024
Giza, Egypt
AMIT Learning
Machine Learning Engineer Intern
June 1, 2022 – August 31, 2022
Cairo, Cairo Governorate, Egypt
Image Compression Using Genetic Algorithms
January 1, 2025 – December 31, 2025
Engineered a novel image compression system based on Genetic Algorithm (GA) optimization, providing an alternative to conventional compression methods such as JPEG and DCT. Designed and benchmarked custom selection, crossover, and mutation strategies (tournament selection, single/multi-point crossover, adaptive mutation) to optimize compression efficiency vs. reconstruction fidelity. Evaluated algorithm performance using PSNR, SSIM, and compression ratio metrics across multiple sample image datasets, demonstrating competitive reconstruction quality at significant size reductions. Produced a comprehensive technical report and visualization dashboard comparing GA configurations, showcasing trade-offs between compression rate and image quality.
WindFarm Power Prediction Under Distribution Shift
January 1, 2024 – December 31, 2024
Developed a machine learning benchmark to predict wind turbine power output and operational outcomes, addressing the real-world challenge of distribution shift between training and deployment environments. Trained and compared multiple regression models (Random Forest, Gradient Boosting, Neural Networks) and evaluated robustness against shifted test distributions using MAE, RMSE, and R² metrics.
Academic Machine Learning Project (Training Project)
January 1, 2023 – December 31, 2023
Designed a complete ML pipeline covering data ingestion, exploratory data analysis (EDA), preprocessing, baseline modeling, and evaluation emphasizing reproducibility and clean code practices. Built baseline classification and regression models, applying cross-validation and confusion matrix analysis to interpret performance reliably.
Cultural Fit Analysis
The candidate's project diversity, including academic research in genetic algorithms and distribution shift, along with practical internships in ML engineering and an AI evaluation role, suggests a broad interest in AI applications. Their eagerness to learn and contribute, as stated in the resume summary, aligns well with a growth-oriented culture. The experience in a global team also indicates an ability to work in diverse environments.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving, fast learning, communication, presentation skills, teamwork, and adaptability, which are crucial for operational fit in a dynamic AI engineering environment. Their experience in collaborating with cross-functional global teams and providing structured feedback indicates good interpersonal and communication skills.