• Own and maintain our data pipeline architectures (e.g., critical data ingestion services, ETL pipelines, database mirroring and warehousing), ensuring they are reliable, monitored, and meet SLAs. • Manage and evolve our data modeling environments and provide a smooth, well-documented workflow for analysts and engineers. • Operate and improve our orchestration systems (Dagster), ensuring jobs run reliably and are observable. • Evaluate and rationalize data tooling from Databricks and notebooks (Marimo, Jupyter) to BI/analytics platforms (Redash and alternatives) and guide Voltus toward a sustainable, coherent data platform. • Implement observability for data systems (logging, alerting, metrics) so issues are detected early and data quality is continuously monitored. • Champion data governance and documentation, making datasets well-defined, trustworthy, and easy to navigate. • Collaborate with analysts, data scientists, and platform engineers to ensure the infrastructure you build is intuitive, scalable, and solves real-world problems. • Lay the groundwork for advanced applications by making Voltus’ data reliably accessible via well-documented interfaces, positioning us to adapt to future ML and AI use cases.

• 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