I am a Data Scientist with experience in building and deploying production-ready machine learning and generative AI solutions in cloud environments. My expertise spans data preprocessing, feature engineering, classical machine learning, deep learning, and LLM-based workflows, including Retrieval-Augmented Generation (RAG), prompt engineering, and model fine-tuning.
I have completed my Master of Applied Computing (Artificial Intelligence Stream) at the University of Windsor, where I strengthened my foundation in advanced AI, distributed systems, system programming, and software engineering. During my studies, I worked on impactful industry and research projects involving NLP, computer vision, fairness in AI, and large-scale data systems.
At Exeevo, I designed and deployed an LLM-powered AI voice assistant using Azure Cognitive Services and RAG to support secure, context-aware enterprise workflows. At MealLens AI, I collaborated on a large-scale computer vision pipeline for automated meal recognition, working with 200GB+ datasets and leveraging models such as SAM and OpenAI CLIP. Previously, as a Junior Data Engineer at Hydroquo, I built and optimized ETL pipelines processing over one million data points annually and developed real-time dashboards for operational analytics.
My technical skill set includes Python, SQL, R, and Java, with hands-on experience using PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, and XGBoost. I am comfortable with MLOps and deployment, including Docker, CI/CD (GitHub Actions, Jenkins), MLflow, and model monitoring, across AWS, Azure, and GCP. I also enjoy translating complex technical insights into clear, actionable outcomes for both technical and non-technical stakeholders.
I am passionate about building scalable, responsible, and impactful AI systems that solve real-world problems. I am actively seeking full-time opportunities in Data Science, Machine Learning, and AI, where I can contribute to innovative, data-driven products and teams.
