AI Platform Engineer

OpenVPN is building out AI tooling across the organization, but the infrastructure, governance, and
operational patterns around it don't exist yet. This is a greenfield ownership role. You won't be inheriting
a playbook — you'll be writing it. You'll define the architectural standards, establish the governance
frameworks, and build the platform layer from the ground up: developer tooling, internal AI-powered
workflows, and the cloud infrastructure (Vertex AI, etc.) that supports it all. If it touches AI at OpenVPN, it
runs through you.

What You’ll Own

AI Developer Tooling & Enablement
• 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
Internal AI-Powered Workflows
• 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
Governance, Security & Compliance
• 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
AI/ML Cloud Infrastructure
• 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)
Cost Management
• 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