• Design and prototype enterprise-grade, AI-driven customer experience demo solutions, translating business logic into structured reasoning frameworks. • Architect robust retrieval, orchestration, and validation systems, building scalable prototypes that tolerate real-world data variability. • Own the transition from early-stage prototype to hardened proof of concept. • Apply deep knowledge of enterprise contact center ecosystems including CCaaS, CRM, ticketing systems, digital messaging, routing logic, escalation flows, QA models, and reporting structures. • Work directly with CX, Operations, Digital, IT, and Architecture stakeholders to understand real-world workflows and constraints. • Convert operational processes into AI-enabled workflows that align with containment, handle time, SLA, QA, escalation rate, and customer satisfaction metrics. • Identify integration patterns across enterprise systems and design practical implementation approaches. • Lead rigorous technical discovery sessions across multi-layered enterprise organizations. • Extract clear architecture and data requirements from ambiguous or politically complex environments. • Identify logic gaps, data fragmentation, compliance constraints, and integration risks early in the engagement. • Document architecture, assumptions, and technical success criteria clearly. • Partner closely with Account Executives to shape technical strategy within enterprise accounts. • Scope engagements realistically, balancing ambition with execution feasibility. • Articulate architectural tradeoffs clearly to both executive and technical audiences. • Influence enterprise stakeholders on integration patterns and AI implementation approaches. • Contribute to scalable solution design that supports long-term account expansion. • Operate independently in fast-moving, ambiguous environments. • Rapidly build working prototypes under tight timelines. • Take full ownership of technical outcomes rather than relying on downstream teams to validate feasibility. • Design with production realities in mind, not just happy-path demonstrations. • Maintain a consistently high conceptual and technical bar in AI-driven systems. • Deliver artifacts that withstand executive and architectural scrutiny.