Principal AI Systems Architect · AI-Native Systems Engineering
I design and build production-grade systems where AI agents, models, distributed software, cloud, and edge infrastructure operate as one engineered system.
My background spans 20+ years of software and systems engineering, with deep experience in distributed systems, backend architecture, high-performance systems, reliability, security, IoT, embedded systems, and edge computing.
Today, my work is increasingly focused on engineering reliable systems around probabilistic intelligence.
- AI-Native Systems Architecture
- Agentic & Multi-Agent Systems
- Distributed Systems
- AI Platform & Control Plane Architecture
- Durable Agent Execution
- Evaluation & Verification
- AI Security & Authority Delegation
- Reliability & Failure Engineering
- Observability & Forensics
- Context, Memory, RAG & MCP
- AI-Native Software Development
- Hybrid Cloud & Edge Systems
A model is only one component of a production AI system.
The harder engineering problems emerge around it:
state · authority · context · verification · failure · observability · reliability · cost
My focus is on designing those boundaries explicitly and turning probabilistic intelligence into systems that can operate safely and predictably in production environments.
I'm currently exploring and building across:
- Agent execution and orchestration
- Tool and MCP architectures
- Context and memory systems
- Verification and evaluation pipelines
- Agent security and permission boundaries
- Durable AI workflows
- AI platform architecture
- AI control planes
- Distributed and hybrid AI execution
- Reliability patterns for nondeterministic systems
The emphasis is on working implementations, architecture decisions, experiments, failure analysis, and measurable evidence.
My engineering experience includes:
- Large-scale distributed and concurrent systems
- High-throughput telemetry and streaming architectures
- Backend and platform engineering
- Reliability and fault-tolerant system design
- Security-sensitive systems
- High-performance data processing
- IoT, RF, embedded and edge systems
- Cloud infrastructure and CI/CD
- Technical architecture and engineering leadership
My repositories increasingly serve as an engineering notebook for AI-Native Systems Engineering — implementations, experiments, benchmarks, architectural patterns, and failure investigations.
As this work develops, the strongest artifacts will be documented and pinned here.