Principal, AI Strategy & Automation Architect

Genesys•Raleigh, NC

22h•$161,500 - $283,900•Hybrid

About The Position

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 Principal, AI Strategy & Automation Architect is a senior technical-strategic role responsible for designing and building the automation, data, and prototype capabilities that enable the NACS AI Strategy Team to scale its impact. This role bridges AI strategy, technical architecture, data engineering, automation, and practical prototyping. The Architect will help transform outcome-led AI methodologies into reusable tools, accelerators, technical patterns, lightweight applications, data workflows, and decision-support assets that improve how internal teams shape, measure, and execute AI strategies. This is not a Product, Sales, or traditional software delivery role. The Architect does not own product roadmap, quota, or production implementation delivery. Instead, this individual supports strategic enablement by building practical internal accelerators, proof-of-concept tools, data automation workflows, and technical advisory assets that help teams evaluate AI opportunities, assess readiness, understand feasibility, measure outcomes, and improve consistency across engagements. The ideal candidate is a hands-on technical builder with strong business context — someone who can work with ambiguity, understand customer experience and AI strategy, and rapidly turn concepts into usable tools, prototypes, automations, and analytical assets.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, Analytics, Mathematics, or a related technical field, or equivalent practical experience.
10+ years of experience in technical architecture, solutions engineering, data engineering, automation, analytics engineering, AI solutions, SaaS platforms, customer experience technology, or related fields.
Strong hands-on development experience with languages and tools such as Python, SQL, JavaScript, APIs, cloud services, automation platforms, BI tools, or related technologies.
Experience building prototypes, internal tools, technical accelerators, dashboards, data workflows, scripts, or lightweight applications.
Strong understanding of APIs, data integration, analytics data, automation patterns, and technical solution design.
Ability to work with ambiguous business problems and convert them into practical technical approaches.
Understanding of AI, automation, LLMs, analytics, data workflows, and customer experience technology.
Ability to communicate technical concepts clearly to business, advisory, and executive audiences.
Proven ability to partner across strategy, technical, advisory, and delivery teams.
Strong judgment around when to prototype, when to automate, when to simplify, and when to recommend a more formal delivery path.

Nice To Haves

Experience with Genesys Cloud APIs, analytics, digital channels, routing, bots, conversational AI, knowledge, predictive engagement, predictive routing, agent assist, or workforce engagement capabilities.
Experience in contact center, CCaaS, SaaS, customer experience, AI, automation, or digital transformation environments.
Experience with cloud platforms, data pipelines, reporting systems, BI platforms, or analytics engineering.
Experience using LLMs or AI-assisted development tools to build internal accelerators, decision-support tools, or workflow automations.
Experience designing technical proof-of-concepts that support strategy, advisory, or transformation work.
Experience with operational metrics such as containment, AHT, FCR, transfer rate, escalation rate, abandon rate, service level, agent utilization, digital conversion, self-service performance, and adoption metrics.
Ability to create polished but practical tools that can be used by non-technical internal teams.

Responsibilities

Partner with AI strategy, analytics, advisory, and delivery teams to translate outcome-led AI methods into scalable technical assets.
Design and build practical tools that support AI opportunity mapping, KPI alignment, readiness assessment, value realization, health checks, journey analysis, and outcome measurement.
Support complex customer and internal scenarios by assessing technical feasibility, data availability, integration considerations, and automation opportunities.
Translate ambiguous strategic needs into working prototypes, data workflows, proof-of-concept applications, and reusable technical patterns.
Help internal teams move from manual, one-off analysis toward repeatable, scalable, and automated methods.
Build internal accelerators, lightweight applications, scripts, dashboards, data workflows, and automation assets that improve team efficiency and consistency.
Develop prototypes that demonstrate how AI strategy, measurement, and operational data can be connected to actionable recommendations.
Create reusable automation patterns for customer health checks, KPI tree generation, value realization scorecards, readiness assessments, use case prioritization, and engagement preparation.
Design tools that help teams synthesize customer context, operational performance, AI adoption data, digital journey friction, and business goals into practical strategic recommendations.
Use AI-assisted development, LLMs, APIs, automation frameworks, and data tools to improve speed, quality, and repeatability of strategic engagements.
Work with Genesys Cloud data, APIs, analytics exports, conversation data, queue/routing metrics, bot and flow data, digital engagement data, and related operational datasets.
Design repeatable data models and workflows that support outcome measurement, baseline analysis, adoption tracking, performance diagnostics, and optimization recommendations.
Partner with strategy and analytics leads to turn KPI frameworks and value models into scalable technical tools.
Assess data quality, reporting maturity, integration constraints, and technical readiness as part of AI strategy and value realization engagements.
Identify where automation, data engineering, or technical design can reduce manual effort and improve consistency across internal teams.
Evaluate practical uses of AI, automation, analytics, and orchestration to improve strategic engagement delivery and internal enablement.
Prototype AI-supported workflows for research, synthesis, discovery, KPI mapping, technical assessment, documentation generation, and recommendation development.
Develop reusable technical patterns that help internal teams responsibly apply AI to customer strategy, advisory, and measurement work.
Stay current on relevant AI, automation, API, data, and analytics trends, with a focus on practical application rather than hype.
Provide technical thought leadership on how AI strategy can be supported by data, automation, and reusable tools.
Contribute to the technical foundation of the AI Outcomes methodology, including outcome intelligence, applied AI, and outcome engineering.
Create technical playbooks, reference architectures, prototype patterns, reusable code assets, automation templates, and enablement materials.
Help define how strategic assessments, value realization models, health checks, and outcome frameworks can be operationalized through tooling.
Partner with the Principal, AI Strategy & Analytics to ensure measurement methods can be supported by practical data workflows and automation.
Mentor internal teams on technical feasibility, data usage, automation opportunities, AI-assisted workflows, and responsible prototyping.
Partner with Professional Services, Customer Experience Advisory, Technical Account Managers, Customer Success leadership, and related teams to provide technical acceleration for strategic initiatives.
Support high-priority customer scenarios by helping teams understand what is technically possible, what data is available, and what automation or measurement approaches may be practical.
Collaborate with execution teams to ensure strategic recommendations are grounded in platform realities, data constraints, and implementation feasibility.
Create internal tools and assets that help teams execute more effectively without replacing delivery ownership.