• Build, maintain, and scale production-ready cloud infrastructure across AWS and Kubernetes. • Support the development of machine learning pipelines and a full data lake architecture. • Improve build automation processes and help move the team from continuous integration to continuous delivery. • Secure, scale, and operate Kafka clusters on Kubernetes. • Partner with Engineering and Security teams to improve infrastructure reliability, security, and compliance. • Develop dashboards, alerts, internal tools, and response processes to identify and address security and reliability risks. • Improve logging, monitoring, and observability across production systems. • Support containerized application deployments using Docker and Kubernetes. • Help evaluate and adopt new tools that improve developer productivity, system reliability, and infrastructure scalability.

• Own the rollout and operational management of AI-assisted development tools across engineering (e.g., Cursor, Copilot, Claude Code) • Define and implement access controls, license management, and usage policies that satisfy SOC2/ISO 27001 requirements • Build cost tracking and reporting so leadership has visibility into AI tool spend and usage patterns across the org • Reduce friction for engineers adopting these tools while maintaining security and auditability • Partner with teams across the org to identify, build, and support internal AI applications such as RAG pipelines, agents, and automation workflows • Evaluate and recommend tooling, frameworks, and patterns based on what teams actually need • Define where IaaS’s responsibility ends and consuming teams’ begins • Advise on data governance policies for LLM usage, including what data can go into which models, where outputs are stored, and how audit trails are maintained • Ensure AI infrastructure and tooling meets existing SOC2 and ISO 27001 controls and can be evidenced in audits • Provide leadership with clear, regular reporting on AI adoption, cost, risk, and usage across the org • Stand up and manage AI/ML infrastructure, primarily on GCP (Vertex AI) within OpenVPN’s existing environment • Design the Terraform modules and IaC patterns for AI infrastructure that follow the team’s existing conventions (e.g., Atlantis-driven GitOps workflows) • Build visibility into AI/ML infrastructure costs and implement controls consistent with how compute costs are managed elsewhere • Evaluate build-vs-buy decisions for AI/ML infrastructure components and managed services with an eye toward operational fit within existing patterns

• Own the rollout and operational management of AI-assisted development tools across engineering (e.g., Cursor, Copilot, Claude Code) • Define and implement access controls, license management, and usage policies that satisfy SOC2/ISO 27001 requirements • Build cost tracking and reporting so leadership has visibility into AI tool spend and usage patterns across the org • Reduce friction for engineers adopting these tools while maintaining security and auditability • Partner with teams across the org to identify, build, and support internal AI applications such as RAG pipelines, agents, and automation workflows • Evaluate and recommend tooling, frameworks, and patterns based on what teams actually need • Define where IaaS’s responsibility ends and consuming teams’ begins – this boundary doesn’t exist yet; you’ll help draw it • Advise on data governance policies for LLM usage, including what data can go into which models, where outputs are stored, and how audit trails are maintained • Ensure AI infrastructure and tooling meets existing SOC2 and ISO 27001 controls and can be evidenced in audits • Provide leadership with clear, regular reporting on AI adoption, cost, risk, and usage across the org • Stand up and manage AI/ML infrastructure, primarily on GCP (Vertex AI) within OpenVPN’s existing environment • Design the Terraform modules and IaC patterns for AI infrastructure that follow the team’s existing conventions (e.g., Atlantis-driven GitOps workflows) • Build visibility into AI/ML infrastructure costs and implement controls (spot instances, auto-scaling policies, idle resource cleanup) consistent with how compute costs are managed elsewhere • Evaluate build-vs-buy decisions for AI/ML infrastructure components and managed services with an eye toward operational fit within existing patterns

• Diseñar, implementar, administrar y soportar ambientes Oracle Cloud Infrastructure (OCI) • Gestionar servicios de networking, cómputo, almacenamiento y seguridad en Oracle Cloud • Monitorear y optimizar ambientes OCI en entornos productivos y no productivos • Atender incidentes, troubleshooting y actividades de soporte de infraestructura • Colaborar con equipos de arquitectura y desarrollo en soluciones cloud escalables • Participar en proyectos de modernización tecnológica y migración a la nube • Documentar procedimientos técnicos y estándares operativos • Garantizar disponibilidad, rendimiento y seguridad de las plataformas Oracle.