OpenVPN Inc.

Platform Engineer

• 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

AI Platform Engineer

Technical Project Manager

• Lead and manage complex projects from initiation through delivery, ensuring adherence to timelines, scope, quality, and business objectives
• Take ownership of driving the software development lifecycle (SDLC) across all phases, ensuring alignment with engineering best practices and security standards
• Establish transparent reporting mechanisms and executive-level status updates to ensure alignment and delivery predictability
• Partner with Product and Engineering leadership to define clear project objectives, deliverables, success criteria, and measurable KPIs
• Facilitate effective communication and collaboration across big cross-functional teams and time zones
• Develop and maintain detailed project and release plans, including resource allocation, dependency mapping, risk management, and contingency planning
• Drive structured planning cadences (quarterly planning, sprint governance, release readiness) across distributed teams
• Proactively identify, quantify, and mitigate risks; escalate when necessary with clear mitigation strategies and decision options
• Continuously improve delivery processes, introducing automation and AI-driven workflows to enhance productivity and quality
• Conduct post-project reviews and implement actionable improvements based on lessons learned.

Senior Project Manager – Technical Project Manager

Artificial Intelligence

• Design and Implement CipherScale AI-Driven SDCL
• Architect and implement CipherScale’s AI-driven software development lifecycle
• Ensure the SDLC process remains structured, traceable, and reliable as automation increases
• Establish scalable patterns for AI-native development across the organization
• Design and implement automated engineering & QA workflows that connect product strategy, engineering execution, testing, and deployment
• Build systems that allow engineering processes to be triggered by structured inputs and outputs while ensuring workflows remain deterministic, reliable, and observable
• Implement mechanisms that prevent failure loops, race conditions, and cascading errors in automated systems
• Design and build agents that assist with engineering execution, validation, and quality control
• Integrate AI capabilities into development workflows while maintaining strict governance and traceability
• Develop reusable patterns for safely incorporating AI into engineering pipelines
• Continuously evaluate and improve the performance and reliability of workflows and agents
• Establish standards and safeguards that ensure workflows remain secure, auditable, and maintainable
• Implement automated validation mechanisms for engineering artifacts, code quality, and system consistency
• Ensure that teams can maintain control and oversight over automated processes and AI Agents
• Measure and optimize engineering cycle times, defect rates, and automation effectiveness
• Continuously refine systems based on feedback from engineering teams and production outcomes
• Ensure the platform evolves safely as automation and AI capabilities expand
• Work closely with engineering, product, QA, and IT operations teams to evolve development workflows
• Ensure teams can effectively and smoothly transition into an AI Native SDLC Workflow while embracing and collaborating with AI-native engineering systems

AI-Native SDLC Engineer

Full-stack Engineer

• Design and Implement CipherScale AI-Driven SDCL Architect and implement CipherScale’s AI-driven software development lifecycle.
• Ensure the SDLC process remains structured, traceable, and reliable as automation increases.
• Establish scalable patterns for AI-native development across the organization.
• Design and implement automated engineering & QA workflows that connect product strategy, engineering execution, testing, and deployment.
• Build systems that allow engineering processes to be triggered by structured inputs and outputs while ensuring workflows remain deterministic, reliable, and observable.
• Implement mechanisms that prevent failure loops, race conditions, and cascading errors in automated systems.
• Design and build agents that assist with engineering execution, validation, and quality control.
• Integrate AI capabilities into development workflows while maintaining strict governance and traceability.
• Develop reusable patterns for safely incorporating AI into engineering pipelines.
• Continuously evaluate and improve the performance and reliability of workflows and agents.
• Establish standards and safeguards that ensure workflows remain secure, auditable, and maintainable.
• Implement automated validation mechanisms for engineering artifacts, code quality, and system consistency.
• Ensure that teams can maintain control and oversight over automated processes and AI Agents.
• Measure and optimize engineering cycle times, defect rates, and automation effectiveness.
• Continuously refine systems based on feedback from engineering teams and production outcomes.
• Ensure the platform evolves safely as automation and AI capabilities expand.
• Work closely with engineering, product, QA, and IT operations teams to evolve development workflows.

AI-Driven SDLC Engineering Lead

QA Engineer

• Design and maintain a scalable automation framework using Python and Playwright, integrating AI tools to accelerate script generation and maintenance.
• Develop automated tests for complex Access Server scenarios, including VPN tunneling, DNS leak protection, and protocol handshakes.
• Leverage Python to automate the provisioning of Linux environments and Virtual Machines for consistent test execution on the most popular cloud providers (AWS, GCP).
• Use AI/ML techniques to analyze test results and system logs, identifying patterns that predict potential regressions before they reach production.

AI Platform Engineer

Senior Project Manager – Technical Project Manager

AI-Native SDLC Engineer

AI-Driven SDLC Engineering Lead

AI-Native QA Engineer, Linux, Networking