I build agent workflows and the evaluation harnesses that keep them honest. The part of AI I care about most is the boring half: measurable outputs, deterministic checks, and systems that are hard to fake. ~1.4 years as an LLM Evaluation Engineer (Turing + contract work) plus an ML internship — shipping Python services, RAG, and agent eval pipelines.
class Aaweg:
role = "Applied AI / GenAI Engineer"
focus = ["agentic workflows", "RAG", "LLM evaluation"]
philosophy = "is it actually good, or just demo-good?"
currently = "shipping eval pipelines and breaking my own agents"
stack = ["Python", "LangGraph", "LlamaIndex", "FastAPI", "pytest", "Docker"]| Project | What it is | Try it |
|---|---|---|
mosaicmind-rag |
Multimodal RAG (PDF/image/audio/video) with LangGraph + LlamaIndex + Chroma · FastAPI · MLflow · Docker | Live demo |
priorityjudge |
Multi-pass LLM-as-judge for plans/PRDs with deterministic citation verification · 9887/10000 on calibration | Quickstart |
mcp-arena |
Reliability leaderboard for MCP servers (conformance, tool reliability, agent-task success) | Repo |
Story-Character-Extractor |
RAG pipeline extracting structured character profiles from stories | Repo |
nibblecore |
4-bit quantization kernels for Apple Silicon, benchmarked vs llama.cpp |
Repo |
swarmbench-codespaces-harness |
Portable Codespaces harness for Harbor SwarmBench agent tasks | Repo |
also: LangGraph · LlamaIndex · MLflow · Airflow · ChromaDB · pytest · SIMD/Metal