Service 003

AI Infrastructure
& Tooling

From LLM proxy configuration and model routing to MCP servers and Claude Code customization, I build the infrastructure that makes AI reliable, auditable, and cost-efficient at scale.

The Problem

Deploying AI in production is more than calling an API. You need model routing to balance cost and capability. You need observability to understand what your AI is doing. You need guardrails that don't kill developer velocity.

Most teams cobble this together with scripts and hope. I build it properly — infrastructure that scales, that you can audit, and that doesn't surprise you with a $50K API bill.

How I Work

  • Deploy and configure LiteLLM proxy for multi-model routing
  • Build MCP (Model Context Protocol) servers for tool integration
  • Create Claude Code skills, plugins, and custom workflows
  • Set up CI/CD pipelines for AI workloads
  • Implement cost controls and usage monitoring
  • Build CLI tools for developer-facing AI operations

What I Build

I've built MCP servers that connect AI to external APIs, CLI tools in Python and Go, Claude Code skills that automate security workflows, and LLM proxy configurations that route between models based on task complexity and cost constraints.

Technologies

LiteLLM ProxyModel Routing
MCP ProtocolTool Integration
Claude CodeSkills & Plugins
Anthropic APILLM Integration
AWS / GCPCloud Infrastructure
GitHub ActionsCI/CD

Related Projects

pantry-agentKroger API MCP Server for AI-powered grocery management demscore-toolsTypeScript SDK, MCP Server, and Claude Code Skill krocliKroger API CLI tool built in Go duocliDuo Security CLI with MCP server and secret store templates openbrainSemantic search knowledge base via MCP protocol

Ready to build your AI infrastructure?

Let's design the foundation that makes your AI tools reliable and cost-effective.

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