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SurakiatP/README.md

Hi there! I'm Surakiat Kansa-ard (Park) 🚀

Junior AI Engineer | Specialist in Agentic AI, Multi-modal RAG & MLOps

I am an ambitious AI Engineer and KMITL graduate (Automation Engineering) with a growth mindset. I specialize in architecting Agentic AI workflows and building scalable, end-to-end solutions that combine Multi-modal RAG, Computer Vision, and production-ready MLOps pipelines.

💻 Technical Toolbox

Category Tools & Technologies
Languages Python SQL C++ Bash
AI/ML & DL PyTorch TensorFlow Scikit-learn XGBoost Pandas NumPy
GenAI & LLMs LangChain LangGraph, Agentic AI, Prompt Engineering, Hugging Face, Ollama, NLP
LLM APIs OpenAI Gemini API, Claude API, DeepSeek API, Groq Cloud API
Computer Vision OpenCV CLIP, EasyOCR, Tesseract, YOLO, Image Classification
Vector & Data Qdrant, FAISS, Chroma, RedisVL, Apache Kafka, Apache Airflow, PyThaiNLP
MLOps & DevOps MLflow DVC Docker DeepEval, Langfuse, GitHub Actions, CI/CD
Backend & UI FastAPI Streamlit Gradio, Matplotlib, Plotly, Grafana, Prometheus

🌟 Featured Projects

🤖 Agentic Research System

Powerful, multi-agent AI research system with semantic caching and intelligent routing.

  • Impact: Executed flawless research plans with 100% Faithfulness & Relevancy, reducing costs to ~$0.09/Query.
  • Tech: LangGraph, FastMCP, DeepSeek, Qwen, Redis (Cache), PostgreSQL (LTM), DeepEval, LangFuse.

🛒 Multi-modal RAG Shop Assistant

End-to-end system for product search via Text, Voice, and Image queries.

  • Impact: Achieved 0% hallucination and 93.5% answer relevance with <1s response time.
  • Tech: CLIP, Qdrant, Groq (Qwen-3 32B), Faster-Whisper, Opik.

📄 Multi-Stage Retrieval RAG System

Bilingual (Thai/English) RAG for corporate policies with hybrid search and reranking.

  • Impact: 96.7% evaluation accuracy using DeepEval (LLM-as-a-Judge) for policy conflict resolution.
  • Tech: Hybrid Search (BM25+FAISS), BGE-Reranker, PyThaiNLP, Docling, Ollama.

🚚 Uber Delivery Time Prediction (MLOps)

Full-lifecycle MLOps pipeline from PoC to automated production monitoring.

  • Impact: Reduced training time by 60% and maintained 95%+ prediction success rate.
  • Tech: XGBoost, MLflow, Apache Airflow, Kafka, Prometheus, Grafana.

📈 GitHub Profile Summary

Summary Card

📫 Let's Connect!

"Engineering robust, autonomous AI systems that deliver transformative results."

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  1. Multi-Stage-Retrieval-RAG Multi-Stage-Retrieval-RAG Public

    Python 1

  2. Multi-modal-RAG Multi-modal-RAG Public

    Python 1

  3. uber-delivery-time-prediction-mlops uber-delivery-time-prediction-mlops Public

    Jupyter Notebook 1