Agentic AI Engineer • Machine Learning Engineer • LLM & RAG Systems
- Building production-ready AI systems with a focus on LLMs, RAG pipelines, and agentic applications.
- Building practical AI products with Python, PyTorch, Hugging Face, FastAPI, Streamlit, and SQL.
- Interested in GenAI, AI agents, fine-tuning, and production deployment.
- Practicing DSA and strengthening fundamentals through from-scratch implementations.
- RAG pipelines — vector retrieval, embeddings, grounded LLM responses
- LLM-powered web apps — FastAPI + Streamlit, deployed and production-accessible
- Transformer systems — implemented from scratch in PyTorch, not just via APIs
- Fine-tuned models — LoRA, PEFT, domain-specific datasets using Hugging Face
- Agentic AI assistants — multi-turn context, tool use, goal-directed interactions
🏠 RealtyIQ View Project
An end-to-end AI platform for property price prediction, semantic search, and conversational listing intelligence:
- Price Prediction — XGBoost regression model with SHAP explainability
- Semantic Search — Embedding-based property search using Sentence Transformers + FAISS
- AI Assistant — RAG-powered chatbot grounded in real listing data (Hugging Face LLM)
- REST API — FastAPI backend with full CRUD and inference endpoints
- Dashboard — Streamlit UI for predictions, search, and chat
Complete Transformer architecture implementation in PyTorch, inspired by Attention Is All You Need.
Built with modular code, custom tokenizer, and sequence modeling training pipeline.
View Project
AI-powered pregnancy risk assistant built with LlamaIndex + Gemini + Streamlit.
Uses retrieval-grounded responses and structured risk classification.
View Project
LLM-powered document Q&A assistant built with RAG, embeddings, LangChain, Groq, and ChromaDB.
Answers questions from uploaded study material intelligently.
View Project
FastAPI + LLM app that explains, debugs, and improves code in multiple languages.
Deployed on Railway.
View Project
Streamlit app that generates structured portfolio content from user input using Groq's Llama 3.3-70B.
View Project
Experimentation repo for open-source LLM fine-tuning, training workflows, and evaluation.
View Project
- LinkedIn: https://linkedin.com/in/syed-ebad-ml
- GitHub: https://github.com/smebad
- LeetCode: https://leetcode.com/u/smebad/
- Kaggle: https://www.kaggle.com/syedmuhammadebad