Role Overview
We are seeking a technically deep, hands-on Solutions Engineer with strong enterprise contact center and customer experience expertise, combined with a startup and builder mindset, to lead the design, prototyping, and hardening of AI-driven solutions for large enterprise prospects.
This is not a traditional demo-focused Sales Engineer role. This role operates as an applied AI systems architect and builder in a pre-sales environment. You will translate enterprise workflows into structured, demo-ready AI architectures and personally build working prototypes that validate feasibility under real-world constraints.
You must be comfortable owning technical design end-to-end, operating with high autonomy, executing under executive visibility, and driving technical outcomes without relying heavily on downstream teams.
What You’ll Do
Applied AI Solution Architecture
- 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.
Enterprise CX & Contact Center Expertise
- 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.
Enterprise Discovery & Technical Navigation
- 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.
Strategic Pre-Sales Partnership
- 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.
Execution Expectations
- 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.
Required Experience
Enterprise CX & Contact Center Background
- 5+ years of experience in enterprise customer experience, contact center, or customer support technology environments.
- Strong familiarity with CCaaS platforms, CRM systems, routing frameworks, digital messaging channels, escalation models, and reporting metrics.
- Experience modeling solutions against operational KPIs including containment, AHT, SLA, escalation rate, QA score, and CSAT.
- Demonstrated experience working with enterprise stakeholders across Operations, IT, Digital, and Architecture teams.
Enterprise Stakeholder Navigation
- Proven ability to lead technical discovery in complex, multi-stakeholder enterprise accounts.
- Experience extracting structured requirements from ambiguity.
- Track record of influencing architectural decisions and technical direction within enterprise environments.
- Comfortable pushing back constructively on scope, feasibility, and risk.
Applied AI & Systems Engineering
- Hands-on experience building AI-driven applications using large language models or similar technologies.
- Experience structuring reasoning flows, orchestration logic, and decision frameworks.
- Practical experience designing retrieval strategies across structured and unstructured knowledge sources.
- Strong Python proficiency including API client development, data normalization, error handling, and test harness construction.
- Experience building lightweight internal tools or utilities to accelerate solution development.
- Experience debugging multi-system interactions across AI behavior, logic layers, and integration layers.
- Familiarity with version control and