Sieon Labs
About Sieon Lee

I build AI agents, retrieval systems, and production data platforms.

My work sits at the intersection of AI agent platforms, retrieval infrastructure, and operational data systems. Sieon Labs is where I turn that practice into source-backed engineering notes for people building real production AI systems.

The common thread is operational reliability: clean data flows, scalable vector search, intent routing, observability, and workflows that remain understandable after the demo.

Agents

Intent routing, tool execution, autonomous workflows.

Retrieval

Vector search, indexing strategy, hybrid data access.

Data infra

ETL, cloud databases, Terraform, monitoring.

Writing

AI infrastructure news, deep guides, AI systems cases.

Super Amplify

AI & Agent Development Intern

Contributing to enterprise AI agent platform features across integrations, execution workflows, productivity surfaces, and intent routing.

  • Built routing logic for Collection Chat, Ask Data, and AI agent workflows.
  • Supported Pinecone migration architecture and document indexing strategy.
  • Contributed to analytics and reporting workflows for operational insight.
Nexon Korea

Data Engineer

Built production data infrastructure for analytics, recommendations, and large-scale game operations.

  • Designed a 500GB+ AWS OpenSearch k-NN vector embedding store.
  • Built Python ETL and SQL Server auditing workflows for production analytics.
  • Provisioned Aurora MySQL, Azure SQL, and Redis with Terraform.
  • Built Grafana dashboards and anomaly-detection pipelines for operational reliability.
LG CNS Europe

IT MES Systems Engineer

Worked on manufacturing systems where query performance and reporting reliability directly affected operations.

  • Refactored SQL procedures behind real-time manufacturing dashboards.
  • Built C# automation tools for reporting integrations.
Education

University of Chicago · Stony Brook University

M.S. Applied Data Science at the University of Chicago; B.S. Information Systems from Stony Brook. Current capstone work focuses on healthcare AI retrieval systems using clinical datasets.

Systems toolkit

Capabilities instead of a resume keyword dump.

  • Python, SQL, R, C#
  • Pandas, NumPy, PySpark
  • Scikit-learn, TensorFlow, PyTorch
  • ETL/ELT pipeline design
  • Batch and streaming workflows
  • Data modeling and quality monitoring
  • AWS, Azure, GCP
  • OpenSearch, Aurora, Redis, BigQuery
  • Terraform, Docker, Kubernetes, Grafana