AI Systems Architect · Founder/CTO · Backend & LLM Infrastructure
I design and build AI/backend systems that move beyond prototypes toward production-grade reliability.
My work focuses on LLM orchestration, backend architecture, observability, reliability, cost control, and knowledge infrastructure.
Currently building an AI product involving structured memory, async backend services, LLM workflows, and personalized user intelligence.
- AI/backend architecture for production systems
- LLM orchestration and agent workflows
- FastAPI and Python backend systems
- Observability, reliability, and cost control
- Evaluation, deployment, and production-readiness for AI systems
- Knowledge infrastructure, memory, state, and signal extraction
A production-minded FastAPI starter for AI/LLM backend services.
Includes route/service/schema separation, provider abstraction, structured JSON logging, request IDs, tests, Docker, and documentation.
A practical checklist framework for evaluating whether AI systems are ready for production.
Covers architecture, LLM workflows, observability, evals, RAG/context design, cost control, security, deployment, and reliability.
Practical architecture notes on production-grade AI systems, LLM orchestration, observability, backend architecture, and reliability.
Explores the gap between AI prototypes and production systems, including workflow orchestration, semantic observability, system boundaries, and architecture review patterns.
A small OpenAI-based extraction workflow for mapping official NACE code information from source documents into structured output.
Originally created as a practical helper script during consulting work.
Founder/CTO. Building a multi-repo AI product with backend services, async workers, Supabase/Postgres, LLM workflows, structured memory, and stateful user intelligence.
A personal tech intelligence system for turning fragmented AI and developer content into structured, high-signal knowledge.
Currently private and under active development.
I am especially interested in the gap between AI prototypes and production systems:
- Structuring LLM workflows beyond prompt-wrapper demos
- Designing observable and evaluable AI systems
- Separating routes, services, providers, schemas, and infrastructure
- Modeling memory, state, context, and user-specific intelligence
- Building AI products that are reliable enough to survive real usage
Python · FastAPI · TypeScript · React · Supabase · Postgres · LangGraph · LangChain · OpenAI SDK · Docker · DigitalOcean · Grafana · PySpark · Databricks