B.Tech student at the National Institute of Technology Tiruchirappalli, learning AI, machine learning, and full-stack development. I work with Python, Java, Flask, and basic front-end tools, and I’m currently focusing on building chatbots and RAG-style question-answering systems as learning projects. I’ve worked with PDF and ticket-style data ingestion, vector-store retrieval, and API-based backends at an internship, and I’m continuing to improve my skills by building small but practical projects and understanding how they work end-to-end.

• NIT Tiruchirappalli, Metallurgical & Materials Engineering.
• ex-AI/ML Intern @ Birlasoft — built a retrieval-augmented Oracle EBS support chatbot with PDF-based document ingestion and workflow diagram automation using draw.io.
• Primary: Python, Java, Flask, FastAPI, Docker, SQL, prompt engineering, vector-store retrieval, embeddings, REST APIs.
• Email: sutaraarya5@gmail.com.
• LinkedIn: linkedin.com/in/aarya-sutar.
• GitHub username: Aarya-Sutar.

I am a B.Tech student learning AI and machine learning while building small, practical systems that actually work. I know front end development, Flask, Python, Java and OOP, and I focus on applying that knowledge to chatbots and simple full stack tools rather than abstract research.

• Currently learning and practicing: retrieval augmented generation, prompt engineering, document and ticket ingestion, and deploying model-backed APIs.
• Building: the Aurenia chatbot (RAG pipeline, PDF and ticket ingestion, relevance filtering) and a few front-end and Flask projects to sharpen full stack skills.
• How I work: prototype fast, iterate with tests and simple deployments, document what I build so others can run it. I prefer solving concrete problems over perfect theory.
• Looking for: internships and real projects where I can learn production practices, improve engineering discipline, and ship features that users can actually use.

If your project needs someone who can bridge ML models and production engineering, I deliver results.

Repo: Aurenia-Chatbot (or aurenia-chatbot)

• Uploads PDFs and extracts text
• Chunks document content with overlap for context retention
• Retrieves relevant chunks using heuristic scoring (token overlap, phrase + fuzzy match)
• Restricts LLM responses strictly to retrieved PDF content
• Detects weak or missing evidence and blocks ungrounded answers
• Falls back to general knowledge only after user confirmation

• Flask backend with session-isolated documents
• Chunk-level retrieval with custom scoring logic
• Intent classification for PDF vs general queries
• Evidence verification to enforce answer grounding
• Local LLM inference via Ollama (llama3:8b)
• Minimal frontend chat UI with safe markdown rendering and PDF upload
• No external vector database or cloud services

Why it matters: Demonstrates how to build a controlled, source-aware LLM application that prioritizes correctness over fluent hallucinations when working with private documents.

Repo: Flask-Blog

• Full-stack Flask app with authentication, CRUD, pagination, profile management, and secure password reset via email
• Good example of clean project structure, basic deployment steps, and README-driven onboarding

• Simon-Game — interactive JS memory game, clean front-end demo
• ToDo — simple task manager showing UI + persistence basics
• Dice-Challenge — JS game demonstrating DOM manipulation and game logic

• Languages: Python, Java, JavaScript, SQL
• Modeling & ML: prompt engineering, embeddings, vector stores, transformers, fine-tuning basics
• Web & APIs: Flask, FastAPI, REST, OAuth basics
• Infra & deployment: Docker, CI automation, simple cloud deployment patterns
• Data: PDF parsing, CSV/Excel ingestion, metadata extraction, ticket system integration

• Python 3.9+
• Ollama installed and running

git clone https://github.com/Aarya-Sutar/aurenia-chatbot.git
cd aurenia-chatbot

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt

• export FLASK_SECRET_KEY=supersecretkey
• export OLLAMA_API=http://127.0.0.1:11434/api/generate
• export OLLAMA_MODEL=llama3:8b

ollama serve
python app.py

Open http://127.0.0.1:5000 in your browser.

Each repo has a full README with endpoints, expected env vars, and demo data.

• Hardening the Aurenia Chatbot for multi-tenant usage and better access controls
• Automating ingestion pipelines to handle noisy enterprise PDFs and email/ticket exports
• Learning production ML ops patterns: monitoring, model versioning, and cost-aware serving

• Star or fork the repos you find useful
• Open issues for bugs or feature requests — I respond faster to concrete, reproducible reports
• Want me to contribute? Send a short PR or an issue explaining the goal and I will review it

I take internships and freelance work related to AI/ML systems and frontend/backend engineering. If you need production-ready AI tooling and clean engineering, email me at sutaraarya5@gmail.com or message on LinkedIn.