Role Description We are looking for a Senior Fullstack Engineer with a strong technical foundation to work on developing an application in the fintech / financial management space, involving external integrations and handling large volumes of data. - Develop end-to-end features (backend and frontend); - Build and evolve REST APIs; - Work with data modeling and query optimization; - Integrate with external APIs (e.g., Plaid); - Ensure code quality, testing, and technical standards; - Participate in architectural decisions. Qualifications - 5+ years of experience as a Full-Stack Developer; - Strong experience with Node.js + TypeScript (4+ years, including API design and production systems); - Solid experience with React / Next.js (3+ years, building scalable frontends); - Practical experience with PostgreSQL (data modeling and query optimization); - Strong experience designing and maintaining REST APIs in production environments; - Practical knowledge of authentication and security (e.g., JWT, access control, common web vulnerabilities); - Hands-on experience with Docker (building images, running containers, managing environments); - Good technical decision-making skills; - Proactive problem solving skills. Requirements - You’ll stand out if you… - Have experience with fintech or financial systems; - Have experience with external integrations and applied AI; - Possess knowledge of performance and scalability. Benefits - A true builder’s environment, with autonomy and real ownership; - Space to test ideas; - A high-performing team and challenging, meaningful projects; - The opportunity to grow with the company and leave your mark. Schedule Mon - Fri 9:00 am - 5:00 pm (East-time). The position is full-time, which means that you must be available every working day between 9 am and 5 pm EST due to time-zone changing according to Daylight saving time in the United States. Our selection process We want you to have a smooth and transparent experience throughout your journey with Globalfy. Here’s what you can expect from our selection process: - Screening; - Interview with HR; - Interview with the Hiring Manager; - Live assessment; - Reference verification. Note: The selection process may vary depending on the position and can include additional stages if necessary.

• 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.