Data & AI Engineer with 3+ years of experience building intelligent systems, data-driven applications, and scalable backend infrastructure. Specializing in AI/ML integration, LLMs, predictive modeling, and cloud-native architectures across Python, C#/.NET, Azure, and modern data platforms.

AI & Machine Learning: Building production-ready AI agents, LLM integration, and intelligent automation systems. Expertise in predictive modeling (CO2 emissions forecasting, diabetes risk prediction), NLP sentiment analysis, and image classification (extreme weather events). Proficient in Python, PySpark, scikit-learn, and XGBoost.

Full-Stack Development: Crafting modern web applications with React and Next.js, developing desktop applications with Java Swing (InvestmentPortfolio), and building interactive dashboards with R Shiny. Strong foundation in JavaScript, HTML/CSS, and responsive design.

Data Engineering & Analytics: Expert in data pipeline development, and deploying ML models in production. Skilled in extracting insights from complex datasets and building data-driven solutions.

Systems & Tools: Advanced Git workflows (branching, merging, conflict resolution), database management (PostgreSQL, Supabase), API development, and deploying scalable systems.

• CO₂ Prediction Model for PEI & NB Potato Farms: Predictive model for CO₂ concentrations using environmental sensor data, benchmarking Linear Regression and XGBoost with five-fold cross-validation to forecast emissions across Prince Edward Island and New Brunswick potato fields.
• Diabetes Risk Prediction: Real-time risk assessment app built with R Shiny, using health indicators (BMI, glucose levels, blood pressure) to predict diabetes risk with an interactive user interface.
• Extreme-Weather-Event-Image-Classifier: ML-based image classification for weather events.

"Driven by a passion for technology and problem-solving, I excel in confronting complex challenges with innovative solutions."