As the Backend Lead for Instanimals, I engineered a multi-dimensional recommendation engine using Python, Pandas, and Scikit-learn, implementing weighted scoring and Cosine Similarity to match users with pet shelters. I led the architectural migration from local storage to a cloud-based Firebase Firestore infrastructure, developing data seeding scripts to populate core collections and managing secure service account integrations. To facilitate frontend communication, I built a Flask REST API with CORS support, creating dynamic endpoints that process real-time cloud data through the recommender logic. Throughout the project, I maintained a professional Git workflow on the feature/recommender-system branch, ensuring project traceability and high code quality through structured commit practices.
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