Hi! I'm Ddumba Abdallah Kato, an AI Platform Engineer focused on production AI and infrastructure. I build the "engines" that allow AI to survive in production — while much of the industry focuses on prompt engineering, I focus on the "Day 2" reality of why inference cost runs higher than projected, why RAG pipeline latency lags, and how to scale a Kubernetes cluster to handle tens of thousands of concurrent agentic requests.

I sit at the intersection of platform reliability and AI system design. My goal is to transform "experimental AI" into enterprise-grade infrastructure that is resilient, observable, and profitable — bridging the gap between model development and production-grade deployment so AI platforms are scalable and cost-efficient from the ground up.

I typically work with engineering teams facing AI systems that work in dev but fail to scale under production loads, high inference costs without a clear optimization strategy, latency and observability gaps in complex RAG pipelines, and infrastructure bottlenecks when deploying GPU-intensive workloads.