Bhavneet Singh

I’m a passionate and curious developer with a strong foundation in full-stack web development, machine learning, and blockchain technologies. With hands-on experience in building smart applications—from AI-powered task managers to decentralized event ticketing platforms—I thrive at the intersection of innovation and functionality.

I enjoy solving real-world problems through code, whether that’s creating secure authentication systems with WebAuthN, deploying intelligent systems using LLMs, or building sleek frontends with React and Tailwind. I’m also deeply interested in Linux, shell scripting, and DevOps practices to streamline development and deployment workflows.

I believe in continuous learning, collaborative problem-solving, and making tech accessible and impactful.

University of Ottawa, Ottawa, Ontario September 2024 – Present

Teaching Assistant

• Mentored Software Engineering and Data Science labs. Provided feedback, ensuring adherence to best coding practices.

• Debugged and optimized student projects in Python, Java, and JavaScript frameworks.

University of Bonn, Germany September 2022 – February 2023

Software Developer Intern – Machine Learning & Big Data

• Engineered a real-time visualization dashboard for agricultural Big Data, reducing data processing time by 40%.

• Built high-performance APIs for large-scale agricultural datasets, optimizing response efficiency with Redis caching.

• Applied causal inference (DAGs) and PCA to analyze crop datasets, leading to more robust feature selection.

• Optimized machine learning algorithms through advanced feature engineering and hyperparameter tuning, leading to an

80.9% prediction accuracy.

Indian Institute of Tropical Meteorology, Pune July 2022 – September 2022

Software Engineer Intern – AI & Cloud

• Designed an automated pipeline to fetch climate data from Google Earth Engine, reducing data extraction time by 50%.

• Developed a RESTful API in Flask, enabling seamless access to climate models for real-time predictions.

• Deployed deep learning models on AWS EC2, improving scalability and reducing inference time by 30%.

University of Ottawa, Ottawa, Ontario - Master of Computer Science