Principal AI Solutions Architect, Customer Success

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.
Advise, Influence & Drive Adoption Lead discovery and strategy alignment — partner with Genesys CX Advisors, Solution Consultants, and customer stakeholders to identify AI use cases, assess feasibility and value, and translate business KPIs into actionable technical priorities Surface the real problem beneath the presenting symptom — use data, evaluation outputs, and systematic analysis to form precise hypotheses and direct where intervention has the most leverage Influence adoption through credibility: when customers are blocked, uncertain, or sceptical, resolve it through evidence and clearly articulated reasoning Translate data-driven findings into executive-ready narratives — present complex AI performance insights, business impact, and recommendations with the clarity and confidence that influences C-level decisions
Design and Architecture Define reference architectures, integration patterns, and data flows for AI-powered experience orchestration — adapting the approach iteratively as customer context and data reveals new priorities Lead process-redesign workshops to create seamless, channel-agnostic CX — facilitated with a consultative approach that builds customer ownership of the solution Ensure all designs comply with Genesys and customer security, privacy, and regulatory requirements (GDPR, PDPA, PCI, HIPAA where applicable)
Prototype and Implementation Deliver rapid POC and MVP implementations using Genesys Cloud AI Studio, CoPilot, Agentic Virtual Agent, and related product suites — moving from hypothesis to working prototype at pace, and adjusting direction when the evidence requires it Integrate Genesys AI components with customer CRM, ERP, and third-party systems Establish implementation KPIs and analytics to measure model and journey performance from day one — not as an afterthought
AI Engineering & Outcome-Oriented Delivery Design and implement evaluation frameworks to measure AI solution quality in production: intent accuracy, retrieval groundedness, response relevance, agent goal completion, and policy adherence Build automated eval pipelines that enable rapid, systematic iteration across prompt variants, guardrail configurations, and model versions Architect agentic systems with precision: define agent topology, design tool schemas, and engineer orchestration logic. Measure success through production adoption and demonstrable outcome improvement — use outcome data as the primary signal for where to focus next
Optimisation and Continuous Improvement Define baseline metrics at engagement start and iterate relentlessly Evaluate solution performance against KPIs and refine designs based on data-driven insights, changing direction quickly when the data signals it Collaborate with Customer Success and Professional Services teams to hand over production-ready assets and roadmaps Codify field learnings into reusable frameworks, evaluation standards, and accelerators that scale capability beyond individual engagements
Governance, Ethics, and Enablement Champion responsible AI design principles and apply guardrails to prevent bias or unsafe responses Adhere to Genesys ethical standards and compliance frameworks Mentor customer, partner, and internal teams to build long-term AI maturity and self-sufficiency — transferring expertise, not just delivering outcomes Feed well-formed, evidence-backed field signal to product and solution teams — precise enough to influence roadmap priorities directly
What We’re Looking For Bachelor’s degree (Master’s preferred) in Computer Science, Information Technology, Data Science, or a related discipline
8–12 years of combined experience across AI implementation, CX/CCaaS platform consulting, or technical solution architecture — demonstrated through overlap and measurable customer impact, not additive year counts across separate tracks
Extensive on-field experience implementing or supporting CX, CRM, or AI orchestration platforms (e.g., Genesys Cloud, Google CCAI, Salesforce, Microsoft, NICE CXone, AWS Connect, ServiceNow, or similar)
Hands‑on experience with agentic AI systems: building, evaluating, or operating LLM‑powered agents in production contexts
Demonstrated experience working with APIs, data pipelines, and modern cloud environments (AWS, Azure, GCP)
Track record of mentoring or developing technical peers and codifying expertise into approaches others can build on
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