Strategic AI Architect, Presales — Enterprise CX

Genesys; Nevada (IA); On-site; USD 135,000 - 237,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Flexible schedules

Work from home opportunities

401(k) matching

Adoption Assistance

Fertility treatments

Job summary

Genesys is seeking a Senior AI Architect, Presales to shape how global enterprises apply AI to customer experience. You will guide architecture decisions, connect Genesys Cloud AI with enterprise data, and lead opportunities from discovery through pilots.

Collaborative, strategic, and technical leadership is essential for delivering scalable, measurable outcomes. Role involves designing AI systems, evaluating models, and creating reusable assets to accelerate adoption across the field and

Qualifications

• 7+ years designing and architecting enterprise AI solutions with ML, NLP, and LLMs.
• Ability to trade off model choice, latency, cost, governance, and security.
• Experience integrating AI with enterprise platforms and RESTful APIs.

Responsibilities

• Architect production-ready AI systems for customer experience use cases.
• Design scalable RAG and enterprise knowledge architectures for accuracy and freshness.
• Establish AI evaluation and observability frameworks linked to business outcomes.
• Engineer contextual AI experiences using real-time data across channels.
• Drive presales engagements with solution architecture and product demos.
• Develop reusable technical assets and enablement for field teams.

Skills

Enterprise AI

LLMs

NLP

RAG

Tools

Cursor

OpenAI Codex

Kiro

Job description

Genesys is seeking a Senior AI Architect, Presales to shape how global enterprises apply AI to customer experience. You will guide architecture decisions, connect Genesys Cloud AI with enterprise data, and lead opportunities from discovery through pilots.

Collaborative, strategic, and technical leadership is essential for delivering scalable, measurable outcomes. Role involves designing AI systems, evaluating models, and creating reusable assets to accelerate adoption across the field and