
Francisco Cabrera developed and automated scalable AI agent deployment workflows on Google Kubernetes Engine, focusing on both infrastructure and documentation improvements. In the kubernetes-engine-samples repository, he delivered an end-to-end solution for deploying containerized agentic AI applications using the Agent Development Kit and a self-hosted LLM served by vLLM, leveraging Terraform for reproducible infrastructure and GitHub Actions for CI/CD automation. In the ai-on-gke repository, he consolidated and migrated tutorial content and JupyterHub deployment guidance, streamlining onboarding and reducing maintenance overhead. His work demonstrated depth in cloud engineering, DevOps, and Infrastructure as Code, using Python, Docker, and Kubernetes.

September 2025 delivered an end-to-end Agent AI deployment workflow on Google Kubernetes Engine (GKE) leveraging the Agent Development Kit (ADK) and a self-hosted LLM served by vLLM. This work provides a scalable, reproducible path for deploying containerized AI agents, combining CI/CD automation, containerization, and IaC provisioning to reduce time-to-market and improve operational control. The deliverables live in the kubernetes-engine-samples repo and establish a solid reference implementation for future AI agent workloads.
September 2025 delivered an end-to-end Agent AI deployment workflow on Google Kubernetes Engine (GKE) leveraging the Agent Development Kit (ADK) and a self-hosted LLM served by vLLM. This work provides a scalable, reproducible path for deploying containerized AI agents, combining CI/CD automation, containerization, and IaC provisioning to reduce time-to-market and improve operational control. The deliverables live in the kubernetes-engine-samples repo and establish a solid reference implementation for future AI agent workloads.
April 2025 monthly summary for GoogleCloudPlatform/ai-on-gke. Focused on consolidating tutorials and reducing maintenance overhead by migrating the HF TGI tutorial and JupyterHub deployment guidance to dedicated repositories, updating documentation, and removing obsolete configurations from the main repo. Resulted in clearer ownership, improved onboarding, and lower risk of drift between code and tutorials.
April 2025 monthly summary for GoogleCloudPlatform/ai-on-gke. Focused on consolidating tutorials and reducing maintenance overhead by migrating the HF TGI tutorial and JupyterHub deployment guidance to dedicated repositories, updating documentation, and removing obsolete configurations from the main repo. Resulted in clearer ownership, improved onboarding, and lower risk of drift between code and tutorials.
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