AI systems engineer & applied-R&D lead : I build the harness around the model: agent loops, context engineering, retrieval, tool access, and the eval infrastructure that decides whether any of it ships.
Full-stack engineer by background (10+ years of enterprise .NET / Angular on AWS), which is exactly why my agents survive production , they're built like systems, not demos.
What I work on:
- 🤖 Agent engineering : plan → act → observe loops with bounded autonomy: budgets, stop conditions, checkpoint gates, and human-in-the-loop escalation. Adversarially reviewed before it's trusted.
- 🧠 Context engineering : deciding what the model sees on every call: system state, retrieved knowledge, tool definitions, memory. Most agent failures are context failures, and that's where I spend my token budget.
- 🧭 Orchestration & routing : multi-provider, tiered / cascade routing optimized for cost-per-successful-task, not cost-per-token
- 🔌 MCP & agent skills : building Model Context Protocol servers and composable skills that give models governed access to real internal systems. Plumbing, not chatbots.
- 📚 Retrieval & open knowledge : hybrid RAG over messy real-world documents; vector search (Qdrant, pgvector) alongside open, markdown-based knowledge graphs the agents can read and maintain
- 📐 Evals as infrastructure : golden sets from real inputs, regression suites, LLM-as-judge where it earns its keep. Observability without evals is just watching things break in higher resolution.
Career arc: AI systems ← Full-stack (.NET / Angular / Node) ← Cross-platform mobile (Flutter : it's been a while) ← Blockchain / Solidity (it's been a long while)
Most of what I build lives behind an NDA. What's public here is a thin slice.
Languages
Backend
Frontend
Data
Cloud & DevOps
