Senior AI Architect

Key Responsibilities

• Design end-to-end AI systems for customer experience use cases.
• Architect reliable, production‑ready AI solutions that combine LLMs, deterministic workflows, tools, and orchestration layers.
• Define how AI interacts across the full journey (self‑service, agent copilot, journey management, and back‑office automation), including fallback strategies, human handoff, and failure handling.
• Optimize retrieval‑augmented generation (RAG) and knowledge architectures to extend Genesys solutions where required.
• Design scalable knowledge and retrieval strategies that ground AI responses in enterprise data.
• Develop approaches for content structuring, chunking, embedding, and ranking to ensure accuracy, relevance, and freshness.
• Partner with customers to align AI outputs with trusted knowledge sources while balancing performance, latency, and governance requirements.
• Establish AI evaluation frameworks and quality measurement strategies.
• Define how success is measured for AI‑driven experiences, including accuracy, containment, customer satisfaction, and business impact.
• Create test sets, evaluation methodologies, and feedback loops to continuously improve performance.
• Translate technical quality metrics into business‑relevant outcomes to support customer decision‑making and adoption.
• Engineer contextual AI experiences that leverage real‑time data and conversation state.
• Design how AI systems incorporate dynamic context such as customer data, interaction history, and external signals.
• Optimize context management and prompt structure to maximize relevance while managing token limits and response quality.
• Ensure AI interactions remain coherent, personalized, and aligned across channels and touchpoints.
• Design for scalability, latency, and cost efficiency in enterprise environments.
• Evaluate and optimize AI solutions for real‑world constraints, including response time (especially for voice), throughput, and cost at scale.
• Make informed trade‑offs across model selection, caching strategies, and architecture patterns to deliver performant and economically viable solutions.
• Ensure designs meet enterprise expectations for reliability and responsiveness.
• Leverage expert‑level knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support pre‑sales activities such as technical discovery, solution design, product demonstrations, sandbox/trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, and success criteria, and to highlight the differentiated value of Genesys AI.
• Partner with account teams and Professional Services to transition successful prototypes into production pilots supporting technical handoff, hardening, KPI alignment, and business outcome validation.
• Develop reusable technical assets and enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide technical feedback and strategic insights to Product Management and Engineering on AI product design, implementation considerations, and customer‑driven enhancements.
• Influence CIO/CTO‑level stakeholders and position Genesys as integral to an organization’s broader IT and transformation strategy.

• Hands‑on experience designing modern AI solutions for customer experience orchestration and contact center use cases using classic ML, NLU/NLP, retrieval‑based systems, LLMs, orchestration patterns, tool use, and agentic approaches.
• Ability to make and defend architecture trade‑offs across latency, cost, explainability, governance, multilingual requirements, and business risk.
• Expertise integrating AI solutions with enterprise platforms, knowledge sources, RESTful APIs, event‑driven architectures, identity systems, and broader cloud ecosystems.
• Ability to design for real‑world constraints including voice latency, fallback paths, throughput, reliability, and secure data access.
• Skill in defining AI evaluation strategies, success metrics, and monitoring approaches across offline and online testing, retrieval…