Data Scientist & AI Engineer building production-ready ML, RAG, and Generative AI systems.

An AI knowledge/reasoning system focused on intelligent information retrieval, memory, and assistant-like workflows.

Explores how AI systems can retrieve, organize, and reason over information in assistant-like workflows.

A full-stack Next.js game with PostgreSQL-backed questions, voting, mode selection, Prisma migrations, seed/import tooling, and database-level duplicate prevention.

Provides an engaging question-based voting game with persistent data and scalable question management.

End-to-end local retrieval pipeline with BM25, FAISS dense search, hybrid ranking, a custom scikit-learn feature reranker, evaluation metrics, CLI tooling, and Streamlit demo.

Improves document retrieval quality by combining sparse search, dense search, and learned reranking.

A lightweight educational vector database built from scratch in Python, featuring dense vector storage, metadata filtering, exact similarity search, graph-based approximate search, and JSON persistence.

Shows how vector databases work internally, including indexing, metadata filtering, and approximate search.

Fine-tuning domain-adaptive Sentence-BERT embedding models for semantic search, retrieval, product search, support ticket matching, and job/CV matching.

Improves matching and retrieval quality by fine-tuning embeddings for specific domains and similarity tasks.

Advanced tokenizer engineering project implementing BPE, byte-level BPE, WordPiece, and SentencePiece-style unigram tokenization, with benchmarking against Hugging Face tokenizers and API/demo tooling.

Explains and implements the core tokenization algorithms behind modern language models.