I am an AI/ML Engineer with over a year of experience building and deploying intelligent systems that solve real-world problems.
I have worked across the full lifecycle of machine learning solutions, from raw data preparation and model development to production deployment and monitoring. I enjoy turning ideas into reliable systems that can scale and deliver measurable business value.
My experience includes data science, forecasting, retrieval-augmented generation, and applied machine learning. I have built pipelines for scoring, prediction, and search, and I am comfortable working with both classical ML and deep learning approaches.
I am especially interested in systems that combine reasoning, retrieval, and automation. I like designing solutions that are fast, robust, and practical, whether they are used for analytics, decision support, or user-facing AI products.
I have hands-on experience with Python, SQL, TensorFlow, PyTorch, FastAPI, Docker, Kubernetes, and cloud platforms. I also work with modern LLM and MLOps tooling to make experiments reproducible and deployments dependable.
I am open to remote, hybrid, and relocation opportunities, and I am looking for roles where I can continue growing as an AI/ML professional while contributing to impactful products and teams.
Built a national school-scoring pipeline merging multiple NCES datasets and SEDA to rate 100,000 schools. Designed a weighted scoring model with state-level normalization and null-safe redistribution. Integrated district-level rankings and top-5 school ingestion. Built an amenity scoring pipeline using OpenStreetMap and PostGIS caching for location intelligence.
Consolidated fragmented datasets into a single analysis-ready schema. Built a weather forecasting pipeline with multiple regression models and an ensemble VotingRegressor. Designed a production RAG system with low retrieval latency and support query deflection.
Implemented regression, classification, and clustering models using scikit-learn and deep-learning models using TensorFlow and Keras. Tuned and evaluated models using standard performance metrics including accuracy, precision, recall, and F1-score.
B.Sc. in Computer Science and Engineering, Computer Science and Engineering
GPA: 3.44 / 4.0. Studied in Menouf, Egypt.
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