• Owning the vNode technical execution: Drive the architecture for how vNode wraps containerd, integrates with the kubelet, and exposes safe isolation primitives. You will set the bar for what ships, what gets deferred, and what gets redesigned. • Going deep on container runtimes and isolation: Lead the work where vNode meets containerd, Kata Containers, gVisor, runc, and the kernel. You will be the person who can explain (and improve) exactly what happens between a Pod spec and a process running under a constrained user namespace with a tight seccomp profile. • Shipping the kubelet integration surface: Own how vNode plugs into the node lifecycle: CRI, kubelet device plugins, cgroups v2, eviction, and the rough edges between Kubernetes' node model and a runtime that does not assume one tenant per node. • Raising the engineering bar: Run technical design reviews, set the pattern for testing isolation guarantees, and mentor the engineers shipping alongside you. You are not a people manager, but you are the engineer the team copies. • Being Customer Zero for vNode: Run vNode against vCluster Platform tenant clusters internally before customers see it. You will close the loop between what AI Cloud operators need and what vNode actually does in production. • Representing vNode externally: Contribute upstream where it matters (containerd, runc, Kubernetes SIG-Node), write the technical posts that explain why namespace-based isolation is the right answer, and represent vCluster Labs at KubeCon-class venues when the timing is right.

• Define and evangelize the AI and agentic flows strategy — aligning business metrics, engineering needs, and platform opportunities. • Build a roadmap for introducing agentic automation features, smart assistants, and AI-driven workflows across all aspects of engineering. • Identify and resolve bottlenecks across the entire product and software development life cycle. • Partner with Engineering, Trust and Security to integrate AI capabilities responsibly and at scale. • Operationalize AI adoption internally and share use cases externally. • Drive complex cross-team projects requiring multi-stakeholder alignment. • Continuously raise the bar for code/architectural quality across engineering. • Lead the delivery of core agentic workflows that improves productivity across engineering. • Balance of hands-on contributions with effective delegation to others. Expect 60% execution and 40% strategy setting. • Drive measurable adoption of AI tooling and flows with internal teams. • Ability to adopt industry standards, ethical frameworks, guardrails, and quality metrics for AI behavior and automated flows. • Define success metrics and KPIs (e.g., AI-driven usage lift, automation ROI, reduction in manual workflows).

• Design the architecture of complex systems; select technologies appropriate to the project scope, considering cost, timeline, and maintainability. • Ensure quality and adherence to standards in code developed by other team members. • Deliver consistent outcomes with a focus on performance, reliability, and scalability. • Diagnose and resolve critical issues in existing systems, often under pressure and tight deadlines. • Implement and promote good testing practices (unit, integration, automation); monitor performance metrics and production incidents. • Translate business requirements into technical specifications and ensure all stakeholders are aligned. • Identify opportunities to optimize processes, reduce operational costs, and improve system efficiency.

• Design, develop, test, deploy, and maintain high-quality backend services and supporting systems. • Work across the stack as needed to deliver complete, end-to-end solutions. • Own services in production, including deployment, monitoring, performance, and operational health. • Collaborate with product managers and designers to translate functional requirements into technical solutions. • Drive architectural decisions with a focus on scalability, reliability, security, and maintainability. • Improve development and delivery workflows, including CI/CD pipelines and infrastructure automation. • Partner with engineers to identify and resolve production issues and system bottlenecks. • Mentor engineers and provide technical leadership through design reviews, code reviews, and architectural guidance. • Help establish best practices for operational readiness, observability, and system ownership.