Toronto, Ontario, Canada
I’m a Master’s in Applied Computing student at Wilfrid Laurier University passionate about building scalable data and AI systems that solve real-world problems. My primary interests lie in Data Engineering, Machine Learning, Distributed Systems, and Generative AI. I enjoy designing end-to-end data pipelines, streaming architectures, and intelligent applications that transform raw data into actionable insights. Recently, I’ve been working on projects involving: Real-time banking transaction intelligence platforms Streaming pipelines using Kafka & Spark ETL orchestration with Airflow Lakehouse architectures Machine Learning optimization & predictive analytics Cloud-based data platforms using Azure I also conducted research on enhancing Random Forest algorithms, achieving 97.96% accuracy while improving computational efficiency through custom feature-prioritized splitting techniques. Before my master’s, I worked as an AI/ML Intern where I gained hands-on experience in data preprocessing, model development, and applied machine learning workflows. I’m currently seeking opportunities in: • Data Engineering • Machine Learning Engineering • Analytics Engineering • Cloud & AI Platforms Tech Stack: Python | SQL | Apache Spark | Kafka | Airflow | Azure | Power BI | Data Warehousing | ETL Pipelines | Machine Learning I’m always open to connecting with professionals, recruiters, and engineers working in data, AI, and cloud technologies.
Built and maintained data pipelines supporting annotation, preprocessing, and model training workflows. Improved dataset reliability through data validation, cleaning, and transformation techniques. Optimized feature engineering workflows for applied NLP tasks, reducing execution time by 25%. Collaborated with cross-functional teams to translate analytical outputs into actionable business insights.
Designed and executed SQL queries to manage relational databases and support structured data systems. Assisted in backend development using ASP.NET and MySQL with focus on workflow efficiency and data integrity.