AI Engineer · LLM, RAG, fine-tuning
I build practical AI systems in Python: question answering over documents with retrieval-augmented generation (RAG), multi-agent assistants, and small language models fine-tuned for a single task. I also work on Turkish-language NLP.
LinkedIn · Hugging Face · Kaggle · Website · Email
| Project | What it does | Built with |
|---|---|---|
| Local RAG Q&A | Ask questions about your own documents, fully on your machine. Hybrid search (semantic + BM25) and streamed answers with sources, evaluated with an LLM judge. | FastAPI, Ollama, ChromaDB |
| Multi-agent assistant | A supervisor agent sends each question to the agents it needs: researcher, analyst, editor. Web search, restricted Python execution and unit tests. | LangGraph, Ollama, SearXNG |
| Zima | Health-advice assistant for people over 70. Synthetic dataset generation and LoRA fine-tuning of Qwen2.5-1.5B. | Unsloth, Transformers, Gradio |
| Finance sentiment | Collects news, RSS feeds and YouTube transcripts about a stock and scores their sentiment with FinBERT. | FastAPI, React, MongoDB |
| Datathon 2025 | Predicts how much each e-commerce session is worth from user events. | pandas, CatBoost |
| ALS recommender | Movie recommendations with collaborative filtering on Spark. | PySpark |
- TurkWeb-Edu Classifier scores the educational quality (0–5) of Turkish web text. It is a Turkish counterpart of the FineWeb-Edu classifier.
- TurkWeb-Edu Student is a 1.5B Qwen model that writes its reasoning in Turkish before it gives the score.
- Zima and its training dataset.
Python · PyTorch · Hugging Face Transformers · LangChain / LangGraph · Ollama · FastAPI · ChromaDB · scikit-learn · PySpark · Docker · AWS