I build data platforms and AI workflows for semiconductor engineering. My background spans hardware and embedded firmware, PCIe PHY validation, SSD systems, and measurement automation. Today, I focus on turning measurement, experiment, and log data into reliable pipelines, useful analysis, and practical automation.
- Data platforms: ingestion and distributed processing, data modeling, metadata and lineage, and data quality.
- Engineering analytics: feature engineering, prediction, anomaly detection, and statistical reliability analysis.
- Optimization and automation: connecting automated measurements with reinforcement learning and Bayesian optimization.
- LLM agents: analysis workflows that connect models to data and engineering tools through LangGraph and MCP.
| Area | Technologies and methods |
|---|---|
| Languages & backend | Python, SQL, C, Java, FastAPI, Spring |
| Data & infrastructure | Spark, Airflow, Kafka, Trino, Iceberg, Docker, Kubernetes |
| AI & analysis | Machine learning, reinforcement learning, Bayesian optimization, statistics |
| Agents & tools | LLMs, LangGraph, MCP, tool integration |
| Hardware & systems | PCIe PHY, signal integrity, NVMe SSDs, embedded firmware, SCPI |
My path started in medical-device hardware and embedded firmware, then moved into semiconductor systems and high-speed interface validation. Since 2023, my work has centered on data engineering and data science, with a growing focus on LLM-based engineering analysis and automation.
I also mentor developers through SSAFY and enjoy sharing knowledge across electronics, software, and data.