Senior AI Architect

At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships.

Help build, support and operate technology used by more than 8,000 organizations in over 100 countries – moving AI from possibility to production in real-world enterprise environments every day.

The Senior AI Architect, Presales will shape how global enterprises apply AI to customer experience, designing architectures that turn advanced capabilities into reliable, scalable business outcomes. At Genesys, you will guide the technical direction of strategic AI opportunities, helping customers connect Genesys Cloud AI with enterprise data, workflows, knowledge, and broader cloud ecosystems.

Operating as a senior presales AI specialist, you will lead solution architecture across self-service, agent assistance, journey management, and back-office automation. Working across LLMs, retrieval-augmented generation (RAG), deterministic workflows, tools, orchestration, enterprise integrations, and agentic AI, you will guide critical decisions involving accuracy, latency, scalability, cost, governance, security, and business risk.

You will partner directly with sales and account teams, customers, Professional Services, Product Management, and Engineering to move complex AI opportunities from technical discovery through validation and production pilots. You will influence CIO and CTO stakeholders, connect architecture decisions to enterprise transformation priorities, and help customers create more relevant interactions, improve operational efficiency, and confidently adopt Genesys Cloud AI.

Your impact will extend beyond individual customer engagements as you help expand AI expertise across the field and bring customer insight into broader product and go-to-market direction. The role provides exposure to strategic enterprise initiatives, platform-level decisions, and emerging AI capabilities shaping the future of customer experience.

• 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
• Establish AI evaluation and observability frameworks that connect accuracy, retrieval quality, tool-call performance, containment, customer satisfaction, safety, drift, cost, and other technical measures to customer and business outcomes
• Engineer 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
• Drive strategic 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
• Demonstrate the value of Genesys Cloud AI by connecting 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

• Extensive hands‑on experience architecting modern AI solutions using the appropriate combination of traditional machine learning, NLU and NLP, retrieval-based systems, LLMs, orchestration patterns, tool use, and agentic AI approaches
• Demonstrated architectural judgment and ability to make and defend tradeoffs across model selection, latency, cost, explainability, governance, multilingual requirements, security, scalability, and business risk
• Deep expertise integrating AI solutions with enterprise…