Most teams ship their first AI features and think the hard part is behind them. It isn't. At Betterworks, we learned this the hard way, not through a single outage, but through reliability failures that were hard to trace, latency problems that only appeared under load, and security gaps that often go undiscussed. In this talk, you'll learn: - Why pipeline reliability must come before latency, quality, and throughput. - The strategic bet that changed everything: an inference-only, model-agnostic approach. - The six architectural layers every production AI system needs. - How to build a feedback loop that actually improves your system over time. - How to manage this while simultaneously migrating from a monolith to microservices. This talk is for data engineers, ML platform engineers, and engineering leaders dealing with the complexity of running AI at scale.