I am a B.Tech Computer Science student specializing in Artificial Intelligence and Machine Learning at Jaypee University of Information Technology, currently working as a Software Development Intern at Black Box. I enjoy building scalable full-stack applications, exploring AI-powered solutions, and solving practical engineering problems through clean, maintainable software.
My work spans React, TypeScript, FastAPI, PostgreSQL, MongoDB, Python, C++, Java, and machine learning workflows. I have built projects across sustainability intelligence, healthcare traceability, attendance management, and adversarial outcome prediction, with a strong focus on usability, system design, and real-world impact.
I am actively learning, building, and contributing as a developer while growing toward strong foundations in full-stack engineering, applied AI/ML, backend systems, and product-focused software development.
Open To
- Software Development internships and early-career engineering opportunities
- Full-stack development projects using React, FastAPI, PostgreSQL, and TypeScript
- AI/ML projects involving prediction, data pipelines, and intelligent systems
- Open-source collaboration, hackathons, and product-focused engineering teams
HydraWatch
HydraWatch is an AI-driven sustainability and infrastructure intelligence platform built to support water-risk awareness, environmental decision-making, and scalable data-backed analysis. The project combines full-stack engineering with applied intelligence to present complex sustainability data through usable product workflows.
| Category | Details |
|---|---|
| Stack | React, TypeScript, FastAPI, PostgreSQL, Python, Data APIs |
| Scale | Designed for region-based analysis, dashboard workflows, and structured environmental intelligence |
| Performance | Optimized API-first architecture with clean data boundaries and fast frontend interactions |
| Security | Environment-based configuration, backend validation, and controlled API access patterns |
| Impact | Helps transform sustainability and infrastructure data into actionable insights |
| Repository | View Repository |
HydraWatch reflects my interest in building software that connects engineering, sustainability, analytics, and AI-assisted decision support. The project focuses on reliable backend services, clean frontend presentation, and real-world data interpretation.
AyurTrace
AyurTrace is a healthcare traceability platform focused on improving transparency, authenticity, and trust across Ayurvedic product and healthcare supply workflows. The system is designed around structured records, traceability logic, and user-friendly access to verified product information.
| Category | Details |
|---|---|
| Stack | React, FastAPI, PostgreSQL, TypeScript, Python |
| Scale | Built for product traceability, verification workflows, and structured healthcare data handling |
| Performance | Modular frontend and backend architecture for fast validation and clear information retrieval |
| Security | Data validation, secure API design principles, and controlled record handling |
| Impact | Supports transparency and trust in healthcare-related product ecosystems |
| Repository | View Repository |
AyurTrace demonstrates product thinking across healthcare, trust systems, and full-stack development. It highlights my ability to take a real-world domain problem and shape it into an application with practical workflows and clear user value.
JUIT Attendance Management System
A full-stack attendance management system designed for academic environments, focused on simplifying attendance tracking, improving record accessibility, and supporting efficient student-faculty workflows.
| Category | Details |
|---|---|
| Stack | React, TypeScript, FastAPI, PostgreSQL, MongoDB |
| Scale | Designed for student, faculty, and administrative attendance workflows |
| Performance | Responsive frontend, structured API layer, and optimized database-backed records |
| Security | Role-aware design principles, backend validation, and controlled data access |
| Impact | Reduces manual attendance effort and improves academic record management |
| Repository | View Repository |
This project strengthened my full-stack development skills through real workflow modeling, database design, frontend usability, and backend implementation. It reflects my interest in building practical systems that solve everyday institutional problems.
Adversarial Outcome Predictor
A machine learning project focused on predicting outcomes in adversarial simulation environments using synthetic data generation, structured feature engineering, and predictive modeling.
| Category | Details |
|---|---|
| Stack | Python, Machine Learning, Pandas, NumPy, Scikit-learn, Synthetic Data |
| Scale | Simulation-driven dataset generation with extensible experimentation workflows |
| Performance | Repeatable preprocessing and model evaluation pipelines |
| Security | Controlled local data workflows and reproducible project structure |
| Impact | Supports data-driven analysis of adversarial scenarios and prediction-based decision modeling |
| Repository | View Repository |
This project reflects an applied ML workflow: define the simulation problem, generate meaningful synthetic data, engineer predictive features, train models, evaluate outcomes, and communicate results through a structured technical implementation.
Black Box 2025 - Present
Working as a Software Development Intern with exposure to software engineering workflows, full-stack development practices, technical collaboration, and production-oriented development standards.
| Recognition | Details |
|---|---|
| Smart India Hackathon 2025 | Participated in innovation-driven problem solving with a focus on practical software solutions |
| IEEE Core Team | Contributing to technical community initiatives, events, coordination, and peer learning |
| Volunteer Educator | Supported learning-focused initiatives through teaching, mentoring, and educational contribution |
| Black Box Internship | Gained professional exposure as a Software Development Intern |
| Project-Based Engineering | Built full-stack and AI/ML projects across sustainability, healthcare, academics, and predictive modeling |
Learning:
- Full-stack application architecture
- Advanced React and TypeScript
- FastAPI backend development
- Applied AI and machine learning
- Database design and optimization
Building:
- HydraWatch
- AyurTrace
- JUIT Attendance Management System
- Adversarial Outcome Predictor
- Recruiter-ready GitHub projects
Exploring:
- AI-powered product workflows
- Scalable backend systems
- PostgreSQL and MongoDB design
- Socket.IO real-time applications
- Open-source collaboration
Open To:
- Software Development internships
- Full-stack development opportunities
- AI/ML project collaboration
- Hackathons and open-source teams
Building reliable software, intelligent systems, and meaningful products with curiosity, discipline, and impact.