Senior Partner AI Architect

## Senior Partner AI Architect Apply locations:
North Carolina, USA:
Texas, USA:
Florida, USA:
Idaho, USA:
Colorado, USAtime type:
Full time posted on:
Posted Yesterday job requisition :
JR110937
** Be the one building AI-powered experiences where they matter most.
** 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
** Genesys Partner AI Architect
** is a is a senior presales AI specialist who operates across four core motions:
1.
Driving strategic Genesys Cloud CX and Genesys AI opportunities with Genesys Strategic Technology Partners, Global Systems Integrators (GSI), and Genesys sales teams showcasing the Genesys differentiated Genesys AI experience.  2.
Scaling the Genesys partner ecosystem through creation of reusable assets, reference architectures, Pre-Sales technical enablement for Genesys partner’s Pre-Sales teams, and coaching.  This role is pivotal in building assets that leverage the strengths of both Genesys and Genesys partners, allowing them to demonstrate the value of their solutions in combination with Genesys Cloud CX and Genesys AI capabilities.  3.
Ensuring that partners understand the Genesys AI business and technical value proposition and are able to independently generate pipeline, articulating value, and delivering AI led outcomes. This is anchored in structured Pre-Sales enablement, repeatable frameworks, and targeted engagement models.  4.
Shaping product and go-to-market direction through real-world partner and customer feedback

This role requires a blend of deep AI technical acumen, exceptional sales & communication skills, and the ability to translate complex capabilities into clear, actionable business value.
*
* Key Responsibilities:

**
* ** Design end-to-end AI systems for customer experience use cases.
** Architect reliable, production-ready AI solutions that go beyond prompt design, combining 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 e informed tradeoffs across model selection, caching…