Building useful software with solid engineering and AI that solves real problems.
$ whoami
> B.Tech CS · Dronacharya College of Engineering · Class of 2028
$ cat mission.txt
> Build full-stack products with AI baked in, not bolted on.
> Ship fast. Learn faster. Break nothing important.
$ ls ~/currently_exploring/
agentic-ai/ distributed-systems/ 3d-web-experiences/
$ echo $STATUS
> Open to Full-Stack / AI Engineer internships & collabs| Field | Details |
|---|---|
| 🎓 Education | B.Tech Computer Science · Dronacharya College of Engineering · 2028 |
| 💼 Experience | Software Development Intern · Hawksvale UK |
| 🏆 Communities & events | Hack2Skill · Commudle · Google Developer events · AMD events |
| 🧩 Problem solving | 200+ DSA problems solved (as reported) |
| 🛠️ Focus | Full-stack engineering · Machine learning · LLM applications |
| 🌏 Location | India |
I’m interested in building AI systems that do useful work inside real products—from ML fundamentals and model experimentation to LLM-powered workflows.
- ML fundamentals: Implemented a Random Forest from scratch, including CART-style splitting, Gini impurity, and bootstrap bagging, for RockShield AI.
- LLM applications: Integrated LangChain and OpenAI/Gemini APIs into AEGIS, a generative-AI stadium operations platform built during a 48-hour hackathon.
- AI-powered tools: Built an Agentic Carbon Tracker powered by the Gemini API.
- Applied AI: Contributed to an AI construction-safety monitoring system during an internship at Hawksvale UK.
I build across the product stack, from interfaces and APIs to data models and deployment.
| Layer | Tools / experience |
|---|---|
| Frontend | React · Next.js · Vite · Recharts · Socket.IO |
| Backend | Node.js/Express · FastAPI · .NET · role-based access control |
| Data | MongoDB · MySQL · PostgreSQL · relational schema design |
| Cloud & delivery | AWS · Google Cloud · Git · GitHub |
| Current learning | LLM engineering · agentic AI · distributed systems · 3D web experiences |
Custom Random Forest implementation with CART-style splitting, Gini impurity, bootstrap bagging, and real-time monitoring.
Focus: ML fundamentals · Python/ML implementation · React · Node.js · Socket.IO
Generative-AI stadium operations project built during a 48-hour hackathon, combining a full-stack interface with LLM-powered workflows.
Focus: LLM applications · LangChain · API integration · Full-stack engineering
Resume intelligence platform that classifies resumes with TF-IDF and Logistic Regression, extracts skills, and scores resume-to-job-description similarity.
Focus: Applied machine learning · NLP features · scikit-learn · FastAPI · Docker
Adaptive AI learning platform that generates personalized curricula, maps knowledge prerequisites, tutors learners, and adapts quizzes to areas of weakness.
Focus: LLM engineering · Gemini API · React · Vite · adaptive learning workflows
AI fairness auditing dashboard with bias analysis, counterfactual what-if simulations, and Gemini-generated explanations and recommendations.
Focus: Responsible AI · fairness analysis · explainability · React · Gemini API
Carbon-tracking project using an AI-powered workflow with FastAPI, Streamlit, and the Gemini API.
Focus: Agentic AI · API integration · sustainability applications · Python
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2024 ── Started B.Tech Computer Science at Dronacharya
2025 ── Deepened full-stack skills · solved 200+ DSA problems
2026 ── RockShield AI · AEGIS · Agentic Carbon Tracker
── Hack2Skill · Commudle · developer events
── Software Development Intern at Hawksvale UK
2028 ── Expected graduation
- Build to solve a real problem, not just to add another technology to a résumé.
- Learn ML fundamentals alongside practical LLM engineering.
- Prefer clear architecture, measurable results, and maintainable code.
- Keep experimenting with full-stack systems, agents, and distributed backends.
