Results-driven Computer Engineer with a strong foundation in Machine Learning, Data Engineering, and Python
backend development. Proven ability to architect end-to-end ML pipelines from data ingestion and preprocessing
t…
Results-driven Computer Engineer with a strong foundation in Machine Learning, Data Engineering, and Python
backend development. Proven ability to architect end-to-end ML pipelines from data ingestion and preprocessing
to model training and production deployment. Skilled in optimizing ML workflows for performance and scalability.
Passionate about applying deep learning, object detection, and real-time processing to build robust,
production-grade vision systems. Seeking a Computer Vision Engineer role to leverage my background in scalable
AI deployment and performance optimization to deliver high-impact, real-time video processing solutions
Led full-cycle development and App Store deployment of three production iOS applications using Swift and SwiftUI.
Architected data persistence layer with Core Data caching and async prefetch strategies, reducing load times by 60%.
Integrated Firebase Authentication and Firestore for secure user management, enabling real-time data synchronization.
Developed and maintained comprehensive automated test suites with XCTest, reaching 90% code coverage and significantly reducing bugs.
Collaborated cross-functionally under Agile, maintaining 100% on-time sprint deliverables.
Led full-cycle development and App Store deployment of three production iOS applications using Swift and SwiftUI., Architected data persistence layer with Core Data caching and async prefetch strategies, reducing load times by 60%, Integrated Firebase Authentication and Firestore for secure user management, enabling real-time data synchronization, Developed and maintained comprehensive automated test suites with XCTest, reaching 90% code coverage and significantly reducing bugs, Collaborated cross-functionally under Agile, maintaining 100% on-time sprint deliverables
Implemented user-facing features and UI components in Swift for fitness tracking app.
Collaborated in Agile sprints with product, design, and QA teams to deliver five major feature releases on schedule.
Maintained comprehensive version control discipline using Git.
Conducted unit and UI testing protocols that identified and resolved 40+ critical issues before production release.
Implemented user-facing features and UI components in Swift for fitness tracking app., Collaborated in Agile sprints with product, design, and QA teams to deliver five major feature releases on schedule, Maintained comprehensive version control discipline using Git, Conducted unit and UI testing protocols that identified and resolved 40+ critical issues before production release
Built an end-to-end retail demand forecasting system using XGBoost (classifier + dual regressors) to predict daily sales and detect spike days with UAE-specific seasonality (Ramadan, Eid, Fri–Sat weekends).Engineered a rich time-series feature pipeline (lags, rolling averages, volatility, price gaps, competitor influence) improving forecasting accuracy and reducing error across product–store combinations.Developed a Streamlit dashboard allowing business teams to upload CSVs, run forecasts instantly, visualize accuracy (MAE/RMSE), and download results — with an auto fallback model for short datasets.Trained and validated the ML models in Google Colab, exported production-ready JSON/Pickle pipelines, and structured the application using an MVVM-style modular architecture for maintainability and deployment
Built and deployed ChurnGuard AI, a Streamlit-based churn prediction platform using XGBoost and Scikit-learn to identify high-risk customersEngineered key behavioral features (usage drop %, engagement consistency, complaint frequency, payment delays) that significantly improved prediction precisionIntegrated SHAP explainability to surface the top churn drivers and provide transparent insights for data-driven business decisionsDeveloped a clean, interactive Streamlit dashboard for CSV uploads, churn scoring visualization, risk segmentation, and automated retention strategy suggestions
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