AI-Native Backend Engineer · Cloud-Native Infrastructure
I build backend systems that think — combining cloud-native infrastructure with AI agent workflows to create reliable, scalable, and intelligent applications.
- 🤖 Designing and deploying AI agents with tool use, memory, and planning capabilities
- 🔌 Building MCP (Model Context Protocol) servers for LLM tool integration
- ⏱️ Using Temporal for durable, fault-tolerant workflow orchestration in production
- ☁️ Architecting cloud-native systems on AWS & GCP with event-driven patterns
- 🔐 Security-minded — clean architecture, secure APIs, maintainable code
- 🤝 Passionate about mentoring and sharing practical engineering knowledge
| Area | Tools & Technologies |
|---|---|
| LLM Integration | OpenAI · Anthropic · Ollama |
| Agent Frameworks | Custom Agents · Tool Use · Memory · Planning |
| MCP Development | MCP Server Design · LLM Tool Integration |
| Workflow Orchestration | Temporal (Durable Execution · Saga Patterns) |
| RAG & Search | Vector Search · Embedding Pipelines |
| Automation | n8n · Webhook Pipelines · API Integrations |
🔁 Temporal workflows that power multi-step AI agent pipelines
🔌 MCP servers that expose backend capabilities to LLMs as callable tools
🤖 Autonomous agents with persistent memory and external tool integration
🌐 Event-driven microservices with real-time LLM streaming support
🔒 Secure, observable AI systems with tracing and audit logging
"AI agents are only as reliable as the infrastructure beneath them."
I design systems where intelligence and reliability coexist — using Temporal for durability, event-driven architecture for scalability, and MCP for interoperability between LLMs and backend services.
Building the infrastructure layer for the AI-native era.