Data & AI Engineer / Architect
I write about production-grade AI, MLOps, data platforms, cloud-native ML systems and the engineering reality behind modern AI.
Notes on reproducible ML, data platforms, observability and delivery systems that teams can actually operate.
Architecture essays, delivery guides and operational lessons from the part of AI work that starts after the first promising notebook.
If you ask whether MLOps is mostly software engineering, the practical answer is broader: delivery, data reliability, architecture and operational ownership usually matter more than the model code itself.
A practical definition of MLOps and a clearer view of where it starts, what it covers and why teams keep reducing it to tooling or deployment.
A practical guide to the systems, delivery and MLOps concerns that start after a model works in a notebook.
A curated slice of the talks that best represent my profile: production AI, MLOps, observability and architecture across modern data platforms.
Embeddings, vector search and the bridge between traditional data platforms and modern AI.
A reference architecture for building one AI platform across BigQuery, Databricks and Vertex AI without creating new silos.
Operationalizing ML in regulated and edge-heavy environments through real production lessons from Azure-based systems.
Designing data observability in Azure so teams know what data exists, how it moves and whether it can be trusted.
Data Architect focused on Azure-native AI/ML platforms, MLOps foundations, delivery pipelines and the engineering systems that survive beyond the proof of concept.
Azure and Databricks architectures built for production pressure.
Delivery pipelines, observability and ML systems that have to stay operable.
A writing angle focused on the engineering layer after the notebook.