AI Engineering Work
From agent orchestration frameworks to production LLM inference on custom GPU hardware - I build AI systems that work in the real world, not just in notebooks.
Hermes Agent Framework
Full AI agent orchestration with intent routing, MCP integration, entity extraction, and a 600K+ node knowledge graph. 4+ microservices with systemd health monitoring.
CodeGraphContext (CGC)
Code knowledge graph toolkit with LSP call resolution, Leiden community detection, Cypher queries, and hot-path risk analysis.
AI Knowledge Router
Multi-system RAG routing that classifies queries and dispatches to GraphRAG, wiki-rag, or codebase-memory with confidence scoring.
Mem0ai Memory Architecture
4-layer persistent memory with NER extraction, embedding quality monitoring, ChromaDB vector storage, and scene block synthesis.
Intel Arc B70 Inference
Production vLLM on 4x Intel Arc Pro B70 GPUs with NVFP4 quantization, tensor parallelism, and Docker toolboxes.
Agentic Architectures Library
35 production-grade agentic AI patterns documented from real deployments with benchmarks and trade-off analysis.
My AI Philosophy
AI systems should be autonomous, auditable, and actually useful. Every system I build ships with verified outputs, comprehensive testing, and clean documentation. No black boxes, no hand-waving. Models are tools - not magic - and production-grade engineering is what makes them valuable.