Designing, building, and operating production-grade AI systems.
I build reliable AI systems that can be deployed, operated, and continuously improved in real-world environments.
My focus is enterprise AI engineering: transforming AI capabilities into scalable and maintainable systems.
- Systems over demos.
- Reliability over hype.
- Documentation is engineering.
- Engineering decisions should leave evidence.
- Identity is stable. Execution evolves.
- Production Voice AI Systems
- Enterprise AI Platforms
- LLM Infrastructure
- AI Observability
- Evaluation and Reliability
Building open-source reference implementations and engineering resources around production AI systems.
Current initiatives:
- Production Voice AI Reference Implementation
- AI System Architecture Patterns
- Production AI Engineering Notes
I document architecture decisions, experiments, trade-offs, and lessons learned while building AI systems.
Topics:
- System Design
- Architecture Decisions
- Performance Optimization
- Observability
- Operations
Python • FastAPI
PostgreSQL • MySQL • Redis
Docker
LLM APIs • Open-source AI Models
Technologies evolve. Engineering principles remain.
- Technical Blog (coming soon)