I am working part-time as an AI software engineer developing our in-house service to improve the accuracy of LLM. This job particularly requires knowledge of natural language processing. I am involved in optimizing LLM prompts, managing the accuracy and quality of outputs, and also working in LLMOps.

I'm using Python, LangChain, Langfuse, LangSmith, WandB, Docker, AWS, Minio, Snowflake, Pytest, and SQL, Fine-tuning LLMs.

Worked on an object detection project using video data obtained from devices produced by the company. The YOLO model was trained on a custom dataset and I developed a simple web app using Python that allows clients to easily start the object detection process and analyze the results. The developed product was demonstrated for clients.

Developed a web application aimed at enhancing the efficiency of internal operations. Employed Python for web development and incorporated fundamental machine learning methods, such as random forest, enabling seamless AI integration accessible to non-technical users.

We formed a team of seven students and participated as an engineer in an activity that was primarily a mobile app as a business. I was mainly responsible for the server-side system. We eventually won the top prize for this activity in the Business Plan Contest held in 2021. I have experience in developing services, mainly mobile apps. I was mainly responsible for the server-side system: we built a push notification system using AWS.

I joined a Kaggle competition, "LLM Prompt Recovery". I'm interested in AI Mathematical Olympiad, LLM 20 Questions, ARC Prize 2024

The team developed a web application in a class at an overseas university. We aimed to create software that allows users to handle data interactively. We plan to add functions such as principal component analysis in the future.

I am compiling papers on LLM in tabular format for my own research. The purpose of this repository is to particularly organize papers from various fields in a comprehensive table. The repo gets 165 stars. 

https://github.com/shure-dev/Awesome-LLM-related-Papers-Comprehensive-Topics