AI Architect (Presales)

Key Responsibilities

• Architect production-ready AI systems for customer experience use cases that combine LLMs, deterministic workflows, tools, orchestration layers, human handoffs, fallback strategies, and failure handling
• Design scalable RAG and enterprise knowledge architectures, including strategies for content structuring, chunking, embedding, ranking, and trusted knowledge integration, to improve response accuracy, relevance, and freshness while balancing latency, performance, security, and governance requirements
• Evaluate and observe AI systems to connect accuracy, retrieval quality, tool-call performance, containment, customer satisfaction, safety, drift, cost, and other technical measures to customer and business outcomes
• Create contextual AI experiences that use real-time customer data, interaction history, conversation state, and external signals to deliver coherent and personalized experiences across channels and touchpoints
• Optimize enterprise AI architectures for scalability, voice and digital latency, throughput, reliability, and cost through informed decisions about model selection, caching, context management, and architecture patterns
• Lead presales engagements through technical discovery, solution architecture, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments that advance enterprise opportunities
• Develop and validate AI prototypes through production pilot readiness, partnering with account teams and Professional Services to prove use cases and integrations, validate latency and success criteria, harden architectures, align KPIs, and confirm business outcomes
• Connect platform capabilities, architecture patterns, integrations, and emerging interoperability approaches, including Model Context Protocol (MCP) and agent-to-agent (A2A), to customer requirements and measurable business outcomes
• Scale field capability by developing reusable technical assets, workshops, coaching, and enablement for Solution Consultants, partners, and account teams
• Influence AI product and go-to-market direction by translating customer requirements, implementation insights, and field feedback into actionable guidance for Product Management and Engineering

Required Qualifications

• 5+ years of relevant experience designing and architecting enterprise AI solutions, with hands-on experience using the appropriate combination of traditional machine learning, NLU and NLP, retrieval-based systems, LLMs, orchestration patterns, tool use, and agentic AI approaches
• Deep expertise integrating AI solutions with enterprise platforms, knowledge sources, RESTful APIs, event-driven architectures, identity systems, and broader cloud ecosystems
• Practical experience with prompt design, context management, RAG, and AI workflow optimization as components of broader production-ready AI architectures
• Proven ability to create and deliver compelling technical demonstrations, prototypes, and presentations that connect complex architecture decisions to customer and business value
• Strong background designing real-time enterprise solutions that account for voice and digital latency, throughput, secure data access, reliability, fallback paths, failure handling, and system scalability
• Demonstrated expertise defining AI evaluation, governance, and observability strategies spanning offline and online testing, retrieval quality, tool-call accuracy, safety, drift, auditability, and cost control
• Strong executive communication, storytelling, and presence, with demonstrated ability to influence CIO, CTO, and senior technical stakeholders

Preferred Qualifications

• Strong understanding of Genesys Cloud and Genesys AI capabilities
• Experience designing AI solutions for customer experience orchestration, contact centers, self-service, agent assistance, journey management, or back-office automation
• Experience creating reusable technical assets and enablement that scale AI expertise across solution consulting, partner, sales engineering, or related field organizations
• Experience partnering with Product Management and Engineering to translate customer and field insights into product or platform improvements