I design and build secure, private backend systems for running open-source large language models locally. My work focuses on setting up dedicated GPU hardware, configuring isolated tool environments, and implementing secure private network overlays so companies can use LLMs without relying on cloud APIs or risking data leaks.

Instead of writing simple API wrappers, I work on the infrastructure side connecting local compute, network security layers, and data pipelines to lower operational costs and keep internal data private.

Technical Domain and Tools:

Infrastructure and Hardware: On-Premise GPU Compute, Local LLM Deployment, Linux Automation

Integration Protocols: Model Context Protocol (MCP), Tool Registries, Execution EnvironmentsData Systems: Knowledge Graphs, Vector Context, Automated Document Ingestion

Networking and Security: Virtual Private Overlay Networks, Isolated Hotspot Routing, OAuth 2.0

Systems Engineering: Containerized Services (Docker), Event-Driven Architecture, Asynchronous Python/TypeScriptCore

Technical Projects: Local LLM Environment: Deployed open-weights models on a dedicated local hardware cluster to move all inference computations completely off the cloud.

Containerized Tools: Configured a registry of over 900 isolated Docker tools using the Model Context Protocol to give local models secure file system access.

Graph Retrieval Integration: Built an ingestion pipeline that maps system directories and markdown files into a localized knowledge graph for contextual retrieval.

Private Network Mesh: Configured a multi-device private network overlay over an isolated cellular gateway, secured by custom token authentication and OAuth.

Asynchronous Monitoring Hub: Built event-driven Signal and Telegram communication gateways tied to background cron jobs for automated system alerts.

I am open to connecting with Infrastructure Engineers, Technical Founders, and Systems Architects working on private local compute and secure backend networks.