Building infrastructure and data foundations for AI

Problem

An agent needs more than a model and a tool loop. It also needs deployed services, controlled access, operational visibility, and dependable data. Repeating that setup for each application makes development and maintenance harder.

My contribution

I built Terraform modules for model-access and observability services, Kubernetes deployment configuration, scoped access controls, and backup automation. These reusable components supplied the infrastructure around AI applications.

I also created agent scaffolding with Pydantic AI and the Pi SDK and automated configuration and delivery with GitHub Actions. The scaffolding gave developers a reusable starting point for new agent applications.

Data foundations

I developed incremental pipelines and analytical models with Python, BigQuery, and Dataform, alongside data-quality workflows with Dataplex. This work covered the data that AI and analytical services depend on, in addition to the infrastructure that runs them.

System boundaries

• Infrastructure: Terraform modules and Kubernetes configuration for deploying shared services.
• Operations: Scoped access, observability, and backup automation around those services.
• Developer tooling: Agent scaffolding and automated configuration and delivery.
• Data: Incremental ingestion, analytical modeling, and data-quality workflows.

These are related engineering responsibilities, rather than a claim that every component was part of a single linear pipeline. The diagram groups them by purpose.

Team credit

These contributions supported shared AI and data services. I describe the components I built without claiming sole ownership of the wider platform.