AI Enthusiast | Sofware Engineer | Machine Learning Engineer

Welcome to my GitHub profile! I'm a 4th-year Management Engineering student at the University of Waterloo, specializing in Artificial Intelligence. I'm passionate about building AI-driven solutions, designing full-stack applications, and exploring the intersection of data science and software engineering.

• Software Engineer Co-op @ Cognite, Austin, Texas
  May 2024 — Sept 2024

  • Developed Cognite’s first industrial agent for the Atlas AI program, improving workflow troubleshooting.
  • Enhanced keyword extraction accuracy by 90% through embedding techniques and cross-encoding.
  • Integrated the Gemini model into Cognite’s doc-parser, boosting document functionality on GCP.

• AI Engineer @ XCare, Toronto, Ontario
  Oct 2023 — Present

  • Fine-tuned Dense CNNs and Vision Transformers, achieving 90% accuracy in X-ray diagnosis.
  • Architected a RAG-AI pipeline for personalized rehabilitation suggestions, referenced by medical professionals.
  • Published and presented research at the Canadian Undergraduate Conference on AI.

• Software Engineer Co-op @ Genellipse Inc., Toronto, Ontario
  Sept 2023 — Dec 2023

  • Optimized MongoDB architecture for vector similarity search, improving data efficiency by 75%.
  • Implemented machine learning models with PyTorch, achieving high accuracy (R2 of 0.85+).

• Languages: Java, Python, JavaScript, SQL, R, C#
• Frameworks: LangChain, LangGraph, HuggingFace, OpenAI, Cohere, JUnit, Express.js, Redux
• Libraries: PyTorch, pandas, NumPy, Scikit-learn, React.js, Node.js
• Tools: Docker, Firebase, Azure, AWS, Git, MySQL, MongoDB, ChromaDB

Capstone Project for MSCI 342

• Tech Stack: MySQL, Firebase, JavaScript, Node.js, React.js, Redux, Express.js
• Developed a full-stack web application that allows users to plan meals based on dietary preferences and allergies, generate shopping lists, and track nutritional info.

Capstone Project for MSCI 446

• Tech Stack: MongoDB, Python, Scikit-learn, PyTorch
• Applied machine learning techniques (Random Forest, XGBoost, LSTM) to predict energy prices in the US PJM Energy market, achieving significant accuracy with the Decision Tree model.

Final Project for MSCI 245

• Tech Stack: MySQL, JavaScript, React.js, Node.js, Express.js
• Built a full-stack clone of IMDB, leveraging React.js for the frontend and Node.js for server-side development.

Self-directed learning project

• Tech Stack: Python, Scikit-learn, MySQL
• Developed an ML model to predict player performance in the NBA based on historical data, applying algorithms such as Simple Linear Regression, K-Nearest Neighbors, and Decision Tree Regressor.

University of Waterloo
Bachelor of Applied Science (Honours Co-op)
Management Engineering, Artificial Intelligence Option
Sept 2021 — Present

• Key Courses: Machine Learning (MSCI 446), Principles of Software Engineering (MSCI 342), Databases & Software Design (MSCI 245), Algorithms & Data Structures (MSCI 240)

• GitHub: jeevanp03
• LinkedIn: Jeevan Parmar
• Instagram: jeevan.prmr
• Email: j29parma@uwaterloo.ca

Feel free to explore my projects and get in touch if you'd like to collaborate or discuss opportunities!