I enjoy building backend systems and understanding how different parts of an application work together.

My background in Mathematics shaped the way I approach engineering:

understand the problem → break it down → build carefully → measure → improve

I started with backend APIs, databases, authentication, testing, caching, and deployment.

Now I am applying that foundation to AI systems, especially Retrieval-Augmented Generation (RAG), where I am building the pipeline from scratch and improving it one version at a time.

A document question-answering backend that processes uploaded PDFs and answers questions using information retrieved directly from those documents.

Document processing runs in the background, allowing uploads to return immediately while parsing, chunking, and storage continue independently.

The system supports follow-up conversations, combines multiple retrieval strategies to find relevant information, and generates answers grounded in the retrieved document context.

The pipeline was optimized from earlier 20–30 second response times to approximately 4–5 seconds for complete question answering.

Repository https://github.com/imLeo007/groundforge-rag

A backend API built to understand how caching can make repeated reads faster while keeping data correct.

The main lesson from this project was simple:

It helped me understand cache hits, cache misses, expiration, invalidation, database consistency, and multi-service backend applications.

Repository https://github.com/imLeo007/user-profile-cache-api

Live API https://user-profile-cache-api.onrender.com/docs

A small AI tutoring application where users can sign in, ask questions, and keep their previous conversations.

This project helped me understand how an AI model fits inside a normal application.

The model generates the answer, while the backend handles:

• users
• authentication
• conversation history
• persistence
• application flow

This became the bridge between my backend development work and the RAG systems I started building afterward.

Repository https://github.com/imLeo007/pocket-Tutor

I prefer learning systems from the inside out.

Understand the purpose
        ↓
Design the architecture
        ↓
Reason through the algorithm
        ↓
Build it
        ↓
Debug each layer
        ↓
Measure what happens
        ↓
Improve the real problem

This is why I am building RAG systems from scratch before depending heavily on higher-level frameworks.

My current focus is on improving retrieval and context quality in AI applications.

I am working toward:

Better Retrieval
      ↓
Context Quality
      ↓
RAG Evaluation
      ↓
Memory
      ↓
Tool Calling
      ↓
Reliable AI Workflows

The goal is not simply to connect models to APIs.

I want to understand how to build AI systems that are useful, grounded, measurable, and reliable.

I am growing toward Backend / AI Engineering, with a long-term interest in building intelligent products that solve real problems.

My main focus is becoming stronger at:

• backend system design
• retrieval systems
• debugging and performance
• AI application architecture
• building reliable products from first principles