Hi, my name is

Neel Shirish More

AI Engineer & Data Scientist

I build intelligent systems that leverage machine learning and large language models to solve complex problems and create impactful solutions.

View My Work Get In Touch

About Me

Hello! I'm a passionate AI graduate from the University of Manchester with a strong foundation in developing end-to-end AI solutions. My expertise lies in building Retrieval Augmented Generation (RAG) systems, agentic AI, and deploying scalable machine learning models.

I enjoy bridging the gap between complex AI concepts and practical, user-friendly applications. When I'm not coding, you can find me exploring the latest in AI research or contributing to open-source projects.

Skills & Technologies

AI & Machine Learning

Large Language Models (LLMs)
Retrieval Augmented Generation (RAG)
Natural Language Processing (NLP)
Python (PyTorch, TensorFlow)
Agentic AI Systems

Data Engineering

SQL & NoSQL Databases
Vector Databases (Pinecone, Chroma)
Neo4j (Graph Databases)
Data Pipelines
Apache Spark

Web Development

Django & FastAPI
React & JavaScript
Docker & Containerization
Cloud Platforms (AWS, Azure)
RESTful APIs

Featured Projects

Knowledge Extractor RAG

Engineered a complete, containerized RAG application with an intelligent agent capable of autonomous planning and hybrid graph-vector retrieval to answer complex queries from user-uploaded documents.

AI: LangChain, Gemini, Cross-Encoder Backend: Python, Django, Redis Database: Neo4j (Graph & Vector) DevOps: Docker, Docker Compose

Multi-Document Text Summarizer

Developed a web-based tool to generate concise, coherent summaries from multiple text sources. The system uses an extractive approach, leveraging sentence embeddings and clustering to identify and compile the most salient information.

AI: Hugging Face, Transformers, RoBERTa NLP: NLTK, Sentence-Transformers Web App: Streamlit, Python Data Science: NumPy, Scikit-learn

CV Question-Answering System

Architected and built a complete Retrieval-Augmented Generation (RAG) pipeline that allows users to chat with their PDF documents. The application processes and indexes documents to provide accurate, context-aware answers to user queries.

AI: LangChain, Hugging Face, Flan-T5 Vector DB: FAISS, Sentence-Transformers Web App: Streamlit, Python Tooling: PyPDF2, ChromaDB

Get In Touch

I'm currently looking for new opportunities and my inbox is always open. Whether you have a question or just want to say hi, I'll try my best to get back to you!

neelmore007@gmail.com