Production ML systems, and AI agents bridging last mile modelling with the cloud infrastructure needed to sustain reliable operations.
Early in my career developing RAG pipelines and forecasting algorithms at Kuona and Entropía, I learned a hard truth: mathematical modeling is only the 'last mile' of enterprise AI. A state-of-the-art model has zero business impact if it suffers from training-serving skew, no lineage of data to model lineage or event tracing in production.
To own true end-to-end impact across the analytics value chain, I built deep platform capabilities, from cloud infrastructure to fullstack integration and from data pipelines to model serving. I partner directly with teams through technical and business discovery to build and ship production AI/ML systems that deliver measurable bottom-line value.
Focus: Bridging last mile AI and ML models to the operational reality of a business.
Scaling an ML platform from a single model to a multi-model catalog requires a relational database to manage state and an API boundary to enforce communication. Here is how I built the CQRS API and network isolation layer to scale from scripts to systems.
Data scientists solve the last-mile problem. ML engineers build the road. Here is what the ML platform engineer's piece actually looks like when built from scratch, grounded in 27 architectural decisions and hard-earned production lessons.
Whether you are a hiring manager recruiting senior ML platform and AI engineering talent, or an existing business exploring platform solutions and architectural discovery, I would love to connect. I engage directly on full-time roles, contract projects, and complex engineering challenges.
Tell me about your team, your open role, or the systems you are looking to build.
