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.

Principal AI Solutions Architect, Customer Success

Professional Services | AI & Digital Solutions | EMEA

As a Principal AI Solutions Architect, Customer Success at Genesys, you will serve as the strategic and technical bridge between customer ambition and successful AI transformation.

You will partner directly with strategic customers across the EMEA region — leading with a consultative approach, moving with the agility that enterprise AI demands, and measuring success against customer business outcomes. At Genesys, we’re transforming how organisations connect with their customers through empathy, collaboration, and innovation.

This role offers the opportunity to make a lasting impact by helping enterprises move from AI exploration to continuous transformation — and to shape how that transformation capability is built and scaled within the organisation.

What You’ll Do

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

Experience

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

Fluency in Dutch and English

Consultative & Customer-Facing Skills

Proven autonomy inside complex enterprise accounts — leading discovery, shaping scope, managing stakeholder expectations, and building trust without hand-holding

Comfortable operating without a defined playbook — able to form and test hypotheses under ambiguity, pivot quickly when the data signals a change, and bring stakeholders along through the process

Translates data-driven findings into executive-ready narratives: quantified outcomes, causal relationships, and clear next steps — not qualitative summaries

Proven leadership in cross-functional environments and complex enterprise contexts

Product instinct: able to define success metrics, surface well-formed requirements, and articulate the business case for technical decisions

Experience with industry verticals such as Financial Services, Healthcare, Insurance, Retail, or Public Sector

Multilingual communication ability is an advantage across the EMEA region

Technical Skills

CX orchestration and workflow design across multiple platforms — with a focus on outcome over architecture elegance

Conversational AI and Agentic Virtual Agent implementation across voice, chat, and messaging channels

Knowledge engineering: retrieval system diagnosis, RAG pipeline design, semantic coverage analysis, and knowledge optimisation for AI consumption

Agentic system design: tool schema authoring, multi-agent topology, prompt engineering as a systematic discipline, and guardrail implementation for enterprise-safe agent behaviour

Applied data analysis: Python for evaluation scripting, log analysis, and integration development; SQL for operational data querying — AI-assisted development tooling expected and encouraged

Deep working knowledge of one or more enterprise CCaaS or Agentic AI platforms — Genesys Cloud preferred, or equivalent such as Google CCAI, Salesforce, AWS Connect, NICE CXone, Sierra, Decagon, or Cognigy; demonstrated expertise in deploying and optimising conversati