AI / LLM
I design the preprocessing, chunking, embedding, and citation flows that drive RAG retrieval quality inside real products.
I narrow problems to the end and leave the reasoning reusable.
I began in data analysis and now build the backend and RAG flows behind production generative AI.
I started out in data analysis and now build Generative AI (K-Bot), a production AI feature in K-System AI, at Younglimwon Soft Lab. I work mainly on Python/FastAPI backends, RAG retrieval with Vector DBs and embeddings, and Azure-based data pipelines — and when needed I build the frontend myself with Svelte and TypeScript.
When I take on a feature, I see it through end to end — reproducing the problem, designing, implementing, and shipping. I reproduce issues precisely, separate state from boundaries, and document the process so it can be reused.
Rather than staying within a single technology, I am growing into an engineer who connects data, backend, and AI to solve real problems. I want to treat technology not as a mere means of implementation, but as a tool to resolve the problems my teammates and the business face more clearly.
Experience
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AI Engineer · Generative AI (K-Bot)
Developed the backend of Generative AI (K-Bot) in Younglimwon Soft Lab's K-System AI, building the full RAG pipeline from document preprocessing to Vector DB structure and metadata design and RAG answer source tracking. I also built MCP servers that let AI agents perform Google Workspace and Microsoft 365 tasks from natural language alone. -
Data Analysis Intern · SCR Process Data Diagnosis
Owned data processing and analysis (30% contribution) on an LS Cable project diagnosing data readiness for SCR wire-breakage factors and process conditions. Analyzed process data to identify crack root causes and patterns, and proposed manufacturing improvements with a baseline prediction model.
What I work with
Backend
APIs, data flows built to be reproducible and traceable.
Database / Vector DB
Storage structures and migrations designed for search, embeddings
DevOps / Infra
I deploy the embedding API on Azure App Service and orchestrate document-ingestion pipelines with Logic Apps and Blob, connecting RAG services reliably to production.
Frontend
I connect backend flows into trustworthy UX, such as verifying the sources behind RAG answers.