Moumen Awad

Architected and scaled predictive AI solutions to optimize international market performance, delivering significant commercial value across 6 global markets. Achieved a significant business impact with the AI solution, converting 27-52% of predictions into actual sales within the initial three market launches, demonstrating strong model performance and commercial value. Strategically optimized data pipeline integrity through automated monitoring, ensuring high-quality data for global business-critical models. Increased codebase scalability and maintainability by refactoring with an Object-Oriented approach and automated internal processes using Python scripts and LLM-based tools. Tech Stack: Python, SQL, Scikit-learn, Spark, XGBoost, Random Forest, LLMs, Docker, Kubernetes, Snowflake, S3, Argo Workflows, Git, CI/CD, Weights & Biases, SonarQube, Jira, Confluence, Agile

Architected and developed scalable microservices by refactoring a monolithic C#/.NET application, improving system modularity and enabling customized deployments for diverse client needs. Engineered and optimized complex SQL workflows, achieving a 30% performance improvement through advanced query tuning and indexing strategies. Deployed and managed robust ETL pipelines to process critical business data, and configured customer-specific databases, ensuring data integrity and availability. Led the end-to-end implementation of a government-mandated e-invoicing system (ZATCA), designing the XML data structure and directly collaborating with clients to deliver customized solutions. Tech Stack: Python, Django, TypeScript, C#, .NET, MS SQL Server, SQL, HTML/CSS, Docker, Git, UI/UX, XML