Forward Deployed Engineering · Production AI · Inference
The deployment gap
is the real job.
AI rarely fails because the model is bad. It fails in the last mile — messy data, unclear ownership, no trust, no rollback. This is about the engineer who closes that gap: forward deployed, customer-facing, building AI that ships into reality.
What this covers
Forward Deployed Engineering
The FDE role decoded — ambiguity tolerance, customer discovery, owning outcomes end-to-end, and the interview system to get the job.
Production AI Systems
The prototype-to-production gap. What breaks when your demo hits a real customer environment, and how to build AI that survives contact with reality.
Inference & Model Serving
Latency, throughput, batching, quantization, and the engineering decisions that make AI fast and cost-effective at scale.
Evals & Reliability
Building evaluation systems that actually catch failure. Observability, trust mechanisms, and the feedback loops that make AI systems trustworthy.
Customer-Facing Engineering
Discovery frameworks, trade-off communication, stakeholder navigation, and the skills that make a technical person effective in a customer room.
Recent writing
All posts →Series
The Deployment Gap
AI rarely fails because the model is bad — it fails in the last mile. This series maps the gap between prototype and production: messy data, unclear ownership, trust, rollback, and everything that kills AI in the real world.
FDE Interview System
The complete system for cracking FDE interviews — ambiguous problem framing, system design under uncertainty, discovery questions, trade-off communication, and the mental model that separates candidates who get offers from those who freeze.
Production Agentic AI
Building agentic AI systems that work beyond the demo — multi-step reasoning, tool use, evals, observability, failure modes, and the engineering decisions that separate a proof-of-concept from a system customers trust.
AI Systems in Practice
LLMs, RAG, fine-tuning, inference optimization, and the full engineering stack for AI in production — written for engineers who want to understand the systems, not just call the API.
Newsletter
Writing on FDE, production AI, and the engineering discipline of deploying AI into reality. No schedule — only when there's something worth saying.