Career
LLM Response Evaluation and Enhancement for Fine-Tuning
- Enhanced the fine-tuning pipeline by systematically evaluating and refining outputs generated by Large Language Models (LLMs), utilizing a rigorous approach to identify model weaknesses and implement optimized responses.
Applied deep linguistic analysis and coding expertise to improve LLM performance in native Korean language tasks, focusing on syntactic, semantic, and contextual accuracy.
Data Engineering and Machine Learning
- Developed a centralized data lake using Apache Kafka for real-time ingestion of aircraft sensor data and Apache Spark for large-scale processing, enabling predictive flight operations
- Implemented a Retrieval-Augmented Generation (RAG) framework by integrating Large Language Models (LLMs) with real-time data sources, allowing for enhanced contextual understanding and predictive analytics in maintenance decision-making
Cost analysis and Optimization
- Conducted regression analyses based on the usage trend over 5 years and suggested a better usage model
- Achieved a 39% reduction* in datalink communication** expense and eliminated duplicated services
- Awarded Model Worker of the Year out of 3,000+ employees (2023)
* Approximately more than $661,000 a year
** Digital messages between aircraft and ground stations or other aircraft, known as ACARS
Project Planning and Scheduling
- Developed and monitored project timelines to ensure timely delivery of milestones, using tools such as Slack, Gantt charts (Excel)
Communication and Reporting
- Established communication channels with stakeholders through regular and ad hoc meetings, emails, and reports
Key Projects
- Scheduling System Examination
- Safety Management System (SMS) Project
- Aeronautical Information System Project
⏶ Gantt chart for planning
⏶ Systems I primarily supported:
Architecture of Flight Operations System