• You are a technical expert who thrives at the intersection of technology and business value. • Whether it's architecting a complex API integration, running a compelling demo, or designing a proof-of-concept that makes engineers say "wow," you've mastered the art of turning technical complexity into customer confidence. • You love the challenge of greenfield opportunities and building something from the ground up. • This isn't your typical Solutions Architect role. As the first-to-funtion, you're at the front lines of WunderGraph's go-to-market motion. • You're the technical voice that helps potential clients see what's possible. The trusted advisor who guides them from "interesting idea" to "let's build this," and the bridge between our product and our customers' real-world challenges. • You know how to navigate technical stakeholders from senior engineers to CTOs, selling on value and building credibility across complex evaluation cycles. • You can balance technical demos with POC implementations and requirements gathering with competitive positioning. • You can write code when needed, review architectures with confidence, and in time and with training explain API federation to a developer or a VP of Engineering with equal clarity. • Most importantly, you care deeply about the customer, their technical challenges, and how WunderGraph can uniquely solve them.

• Lead technical discovery with customers to understand workflows, constraints, data realities, and success criteria—and translate that into a clear technical plan. • Architect end-to-end AI systems (GenAI + traditional ML when needed), spanning data ingestion, retrieval, orchestration, evaluation, deployment, and UX. • Design and deliver rapid prototypes that make abstract ideas concrete and de-risk key decisions (cost/latency/reliability, integration feasibility, quality thresholds). • Develop reference architectures and technical frameworks that demonstrate what’s possible with current and emerging AI technologies. • Guide clients through the AI landscape—capabilities, limitations, tradeoffs, and what it takes to operate AI systems in production over time. • Partner with sales teams to translate technical concepts into clear value propositions, proposals, business cases, and technical presentations. • Define technical requirements and scalable architecture patterns for production-grade systems (security, governance, observability, evaluation, and reliability). • Collaborate with fulfillment to ensure solutions align with customer needs and leverage Tribe AI’s capabilities—and help create smooth handoffs from pre-sales to delivery. • Stay current on frontier developments, bringing in SMEs from the Tribe network and collaborating with partners (including OpenAI/Anthropic) to keep customers ahead of the curve. • Act as a trusted advisor to technical and executive stakeholders navigating AI implementation and adoption challenges.

• Aid in deploying AVD environments using Nerdio Manager, following established processes and best practices. • Provide technical support and troubleshooting assistance to customers throughout their AVD deployments and beyond. • Demonstrate proficiency in Intune, FSLogix, and Active Directory (AAD, Azure AD DS, AD DS) to address customer needs. • Collaborate with cross-functional teams, including internal and external partners, to ensure successful AVD deployments and support.

• Partner with Sales and Customer Success to deeply understand customer requirements, translate them into technical solutions, and serve as a trusted advisor from initial evaluation through deployment and expansion • Lead technical discovery to map customer workflows, identify automation opportunities, and design agent architectures that deliver measurable value • Build and optimize AI agents—configuring workflows, writing and refining prompts, and tuning agent behavior to meet customer requirements • Own integrations between Zip AI agents and customer systems (ERPs, CLMs, ticketing, TPRM, and other procurement tools) using middleware and iPaaS platforms • Drive successful implementations end-to-end—scoping requirements, documenting architecture, testing configurations, troubleshooting issues, and ensuring smooth go-lives • Enable customer teams to manage and improve their agents, building confidence and long-term self-sufficiency • Gather insights on how customers are deploying AI agents, identify emerging patterns, and deliver structured feedback to Product and Engineering