Parth Patel
MS Software Engineering @ SJSU | AI & Cloud | Full-Stack Developer (Python, Java, React) | AWS & Azure
- Role
- Research Assistant at San José State University Research Foundation
- Location
- San Jose, CA, US
- LinkedIn followers
- 500 followers
About Parth Patel
Website: a Master\'s student in Software Engineering at San Jose State University with experience building full-stack and backend systems that are actually used in production.At Deloitte, I worked on cloud-native and data-focused projects for public health and government teams, modernizing legacy platforms, automating workflows, and improving real-time data access. I used technologies like Python, C++, TypeScript, React, and deployed infrastructure on AWS and Azure.I like building clean, efficient tools that solve real problems. Recently, I built:• TenMunches: An AI-driven platform that helps users discover hidden food and drink spots across San Francisco by analyzing reviews and 200+ articles.• QuizMaster: A dynamic quiz generator that automatically creates and evaluates custom quizzes based on user-uploaded content using AI.• An intelligent code feedback system that delivers personalized code insights to students and is actively used in 8 university classrooms.I’m comfortable across the stack—from frontend development in React and TypeScript, to backend APIs, CI/CD pipelines, and cloud services in AWS and Azure.Right now, I’m exploring building scalable SaaS tools and applied-AI opportunities. I\'m especially interested in roles focused on:•Backend / API Development•Full-Stack Engineering•AI / ML Integration•Cloud Infrastructure (AWS, GCP, Azure)Always happy to connect—whether you\'re hiring, building something cool, or just want to swap opinions on the best food spots in SF.
Experience
Research Assistant
San José State University Research Foundation
May 2025 — Present · San Jose, CA, US
Architected an AI feedback model (Python, AWS, MySQL) that analyzed coding assignments, delivering automated feedback to students.• Implemented a Python, Flask production backend, optimized to return code feedback in under 2 seconds, ensuring students received real-time feedback on assignments.• Reduced model training time by 40% through PyTorch and Tensorflow hyperparameter tuning, enabling researchers to prototype and deploy improved feedback models faster.• Achieved 100% success on 5 production releases by engineering a Dockerized inference API on AWS EC2, ensuring reproducible environments across dev, test, and prod.
Education
San José State University
Master's degree
2025 — 2026
University of Toronto
Bachelor of Science - BS
2018 — 2022
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