What You’ll Do

Advise, Influence & Drive Adoption
- Lead discovery and strategy alignment – partner with internal 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 – resolve blockers, uncertainties or scepticism with evidence and clearly articulated reasoning.
- Translate data‑driven findings into executive‑ready narratives – present complex AI performance insights, business impact, and recommendations with 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 reveal 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 – using outcome data as the primary signal for next focus.
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 change.
- 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.
- 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).
- Hands‑on experience with agentic AI systems: building, evaluating, or operating LLM‑powered agents in production contexts.
- Demonstrated experience 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.
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.
Technical Skills
- CX orchestration and workflow design across multiple platforms – 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, 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.
- Deep working knowledge of one or more enterprise CCaaS or Agentic AI platforms – Genesys Cloud preferred.
- Data and integration expertise: REST APIs, event‑driven architecture, JSON.
- Cloud infrastructure familiarity: provisioning, access control, and cost management (AWS preferred).
- Data governance, security compliance, and responsible AI design principles.
Languages & Location
- German and English fluency (other languages an advantage across the EMEA region).
Genesys is an equal opportunity employer committed to fairness in the workplace. We evaluate qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, marital status, domestic partner status, national origin, genetics, disability, military and veteran status, and other protected characteristics.

EEO Statement

Genesys is an equal opportunity employer committed to fairness in the workplace. We evaluate qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, marital status, domestic partner status, national origin, genetics, disability, military and veteran status, and other protected characteristics.

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8310 Germany - Genesys Cloud Services Germany GmbH