Zain Bharde

About

About Me

I'm a senior at Texas A&M University pursuing a B.S. in Data Engineering with a Computer Science minor, currently based in Houston, TX. During my time at Texas A&M, I have developed a strong foundation in Data Science and Machine Learning, along with core knowledge in Statistics and Data Engineering principles.

I started programming in high school and have been hooked ever since. As of now, I have the most fun experimenting with modern AI tools like Claude Code and Cursor, building projects with modern full-stack frameworks, and applying these skills to solve real user problems. My most recent project is Umari: a full-stack SaaS platform that helps small food vendors accept online orders through shareable links or QR codes, with real-time order management and payments.

When I’m not coding you’ll probably find me playing basketball, traveling to a new city, or trying a new resturant. Houston hands down has the best food scene in the U.S. with New York being a close second. San Francisco has always been my favorite city to travel to due its versatility of nature, city, and community.

Technical Skills

Experience

Dematic

May 2024 - Present

Software Development Engineer Intern

- Developed pallet storage logistics software using Java that enabled warehouse robots to select the most optimal pallet storage container, improving selection accuracy by 25% and reducing retrieval time by 15%

- Implemented 15+ interfaces using Nocobase and SQL, enabling warehouse operators to efficiently update, insert, and delete data from AMR and PLC node databases, improving data management efficiency by 20%

Home Depot

May 2025 - Jul 2025

Software Engineer Intern

- Improved P95 API response times by 90% and cut cloud costs by 50% by migrating core sourcing service from Java to Go

- Optimized system performance by reducing memory usage 35x and CPU consumption by 40% on average across load levels

- Delivered project 1 week early and presented final readout to 100+ associates including executives, managers, and engineers

Texas A&M University

Jan 2024 - May 2024

Undergraduate Research Assistant

- Developed a Python script achieving 1.38% error in image distortion removal and contributed to a C++ visual odometry pipeline

- Presented DinoV2's impact on feature tracking for visual odometry at A&M Science Symposium to a non-technical audience

CLOUDSUFI

Aug 2023 - Jan 2024

Data Engineering Intern

- Deployed Google Cloud Cortex Framework for SAP and developed Looker dashboards for intuitive data visualization

- Acquired expertise in data engineering, complemented by certifications in Google Analytics, Machine Learning, and Looker

Projects

Umari

Umari is a deployed web-based ordering platform for small food vendors that supports shareable links and QR codes, offering seamless menu browsing, real-time order management, and Stripe Connect powered payouts through a responsive, production-ready UI.

Next.js React.js Typescript Tailwind CSS Supabase Stripe

Aggie AI

Built a Next.js platform serving over 1,000 monthly users, using RAG-based AI to recommend courses based on GPA distributions and professor reviews from a PostgreSQL database.

Python Next.js React.js PostgreSQL Tailwind CSS

Personal Website

Developed professional portfolio website using HTML, CSS, and JavaScript to showcase experience, projects, and resume with responsive design and optimized user experience. Deployed via AWS using S3, Route 53, and CloudFront to ensure scalable hosting and content delivery.

JavaScript HTML CSS AWS

Great Smiles

Architected AI dental agent using FastAPI, OpenAI Whisper, ElevenLabs TTS, and Twilio with LangGraph state management to automate patient calls intake through multi-agent flows with React.js patient call information dashboard.

Langraph OpenAI API ElevenLabs TTS Twilio

Trivial NCAA Predictions

Utilized the Random Forest machine learning algorithm to predict wins and losses in men's NCAA basketball games based on trivial factors, and constructed the corresponding dataset.

Python Scikit-learn Pandas NumPy Matplotlib

Stock Close Price Predictor

Developed a machine learning model incorporating a Long Short-Term Memory Network to accurately predict stock closing prices, leveraging Numpy, Pandas, and Matplotlib for data handling and visualization.