REVIEW BRIEF

Frontier LLM Engineering Proficiency

Doeon Kim — Demonstrated capabilities across LLM development, fine-tuning, and production deployment

Tool-Calling Reliability

Built and published stage-pilot, a tool-calling reliability runtime for AI SDK applications.

Component	Detail
npm Package	@ai-sdk-tool/parser — published and maintained
Test Coverage	1,720 tests across parser, runtime, and integration suites
Architecture	Streaming-first design with structured output validation
Compatibility	Works with OpenAI, Anthropic, Google, and open-source models

Fine-Tuning & Benchmarking

tool-call-finetune-lab — QLoRA fine-tuning of Qwen2.5-7B for tool-calling tasks, evaluated on the BFCL benchmark.
End-to-end pipeline: data preparation, LoRA adapter training, merged model export, and quantized inference.
Systematic comparison of base vs. fine-tuned performance on structured function-calling scenarios.

Multi-Agent Systems

Nexus-Hive — LangGraph-based multi-agent NL-to-SQL copilot with governed analytics across Snowflake and Databricks.
AegisOps — Multimodal incident analysis platform using TypeScript, React, and Gemini with live demo deployment.
enterprise-llm-adoption-kit — Production LLM governance with RBAC, evals, PII redaction, and FastAPI backend.

LLM Skills Matrix

Category	Skills
Model Training	QLoRA, PEFT, Hugging Face Transformers, PyTorch
Agent Frameworks	LangGraph, LangChain, AI SDK, custom orchestration
Evaluation	BFCL benchmark, tool-call accuracy, structured output validation
Production	Streaming parsers, reliability runtimes, governance, PII redaction
Multimodal	Vision + text incident analysis, Gemini integration

Key Differentiator

Full-stack LLM expertise from fine-tuning foundation models to publishing production-grade npm packages, with 1,720 tests proving reliability at scale.

stage-pilot Fine-Tune Lab