OpenVPN Inc.

• Collaborating with product manager to refine feature specifications • Development and support of Linux-based server software (Python mainly) • Development of a web-based API (XML-RPC and REST mainly) • Collaboration with other developers on development and deployment • A working knowledge of python asynchronous programming

• Develop automated QA processes for server/client-based applications • Document and maintain QA processes and test matrices • Develop and implement testing plans for new features • Implement and integrate regression tests into automated environments • Define acceptance criteria for developed artifacts • Coordinate QA tasks with other teams • Promote a culture of quality throughout the organization

• Communicating with the clients of Access Server & CloudConnexa, our cutting-edge corporate VPN solutions used by thousands of businesses worldwide • Investigate client issues received via our ticketing system • Respond to clients to ensure their full satisfaction • Coordinate with other team members as well as other teams to resolve or refer more complicated issues

• Assist in designing, implementing, and maintaining scalable, fault-tolerant systems that leverage cluster orchestration and containerization technologies, with guidance from senior engineers. • Work alongside the Software Engineering and QA teams to support deployment processes for microservices-based architectures. • Help build and maintain CI/CD pipelines that support container-based application deployment and rollback. • Support system availability by running health checks and contributing to zero-downtime deployments. • Participate in a supported on-call rotation, escalating critical system issues appropriately as you build incident-response experience. • Collaborate with information security teams to follow industry best practices and compliance requirements. • Use AI-assisted tooling (e.g., LLM-powered coding assistants, chat-ops agents, scripted automations) to accelerate routine tasks such as log triage, runbook execution, ticket drafting, and code review.

• 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

• Provide technical assistance and guidance during the pre-sales & post-sales process by identifying customers' technical requirements, developing solutions, and addressing any technical concerns. • Assist the customer success and technical support team in handling general support tickets and online chat sessions. • Collaborate with sales teams to design and propose comprehensive networking solutions that meet customer needs and align with company offerings. • Conduct product demonstrations and presentations to showcase the features and benefits of networking solutions to prospective customers. • Create customized demonstrations with integrations that meet the prospect’s use case. • Assist in responding to Requests for Proposals (RFPs) and Requests for Information (RFIs) by providing detailed and accurate technical information. • Engage with customers to understand their business objectives and technical requirements, providing insights on how our solutions can address their specific challenges. • Assist the Customer Success team with regular briefings for key customers to demonstrate product value, increase feature adoption, and provide information on upcoming releases. • Contribute to the product video collection on YouTube by making short configuration and use case videos. • Provide training and support to internal sales teams and external channel partners on networking products and technologies to enhance their technical knowledge and sales effectiveness. • Stay updated on the latest networking technologies, industry trends, and competitor products to provide informed recommendations to customers and sales teams. • Gather customer feedback and provide input to the product management and development teams to improve product offerings and address market demands.

• 사전 판매 및 후 판매 프로세스에 대한 기술 지원 제공 • 고객 요구 사항을 이해하고 솔루션 개발 • 고객의 비즈니스 목표에 대한 통찰력 제공 • 정기적인 고객 브리핑 지원 • 고객 피드백 수집 및 제품 관리팀에 입력 제공 • 제품 동영상 콘텐츠 제작

• 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

• 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.