I’m a Computer Science student and backend-focused developer specializing in AI-powered workflows, data handling, and API-driven systems.
I work on projects involving FastAPI backends, AI integrations, data validation, and automation pipelines. I have hands-on experience building systems where AI assists with tasks like content generation, data processing, and structured uploads, while ensuring accuracy and reliability.
My strength lies in understanding workflows end-to-end — from raw data or inputs to clean, production-ready outputs. I communicate clearly, follow structured processes, and focus on delivering practical results rather than overengineering solutions.
I’m comfortable learning new tools quickly and collaborating with clients to execute tasks efficiently and correctly.
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- PDF text extraction and preprocessing
- Question answering based only on document context
- LLM integration for semantic understanding
- Backend API for chat interaction
- Structured responses with clear relevance to the source document
ChatWithPDF-AI is an AI-powered backend application that allows users to upload PDF documents and ask natural language questions about their content.
The system processes PDF files, extracts text, and uses an LLM to generate accurate, context-aware responses based strictly on the document content. Prompt constraints are applied to prevent hallucinations and ensure answers remain grounded in the uploaded file.
Key features include:
This project demonstrates real-world AI integration for document understanding, focusing on reliability, controlled prompting, and backend-driven workflows rather than generic chatbot behavior.
- Natural language to SQL conversion
- Prompt-engineered query generation using LLMs
- Backend execution and result validation
- Structured tabular output for easy interpretation
- API-driven architecture designed for extensibility
QueryForge AI is a backend-focused application that allows users to ask questions in natural language and automatically converts them into accurate SQL queries, executes them on a database, and displays the results in a structured table format.
The system uses an LLM running locally via Ollama to generate SQL queries based on user intent. Strong prompt engineering techniques are applied to ensure valid, safe, and optimized SQL output. Generated queries are verified and executed through a backend API before returning the final results.
Key features include:
This project demonstrates practical AI integration into real backend workflows, focusing on correctness, safety, and real-world usability rather than pure experimentation.
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