Director, Product Management, Platform

Do you love creating strategies and vision that can take a diverse portfolio of products and make them all run like a well-oiled machine? If so then NiCE is the place for you. 

This Director of Product Management will lead the strategy, vision, and execution for the CXone platform — the shared foundation of services that accelerate application teams' time to market. As the contact center industry undergoes its most significant transformation through AI and automation, this leader will define how AI-native capabilities are prioritized, built, and delivered across the platform. Managing a team of Product Managers, they will own the platform roadmap, drive cross-functional alignment, and ensure the platform evolves to meet the demands of a rapidly changing AI landscape. 

A Typical Day Might Include the Following: 

• Defining and driving the platform strategy and AI enablement roadmap — including AI-aware extensions to shared services such as the API facade, security and compliance, telemetry, tenant management, and billing framework — to accelerate application teams' time to market for AI-powered features. 

• Leading rapid prototyping and proof-of-concept initiatives with engineering to validate platform service concepts — testing assumptions on scalability, integration patterns, and developer experience before committing to full roadmap investment. 

• Developing and prioritizing product roadmaps and cross-solution backlogs, balancing market demand, customer needs, and business objectives while managing cost margins for a multi-million dollar solution set. 

• Driving spec-driven product development — authoring platform API contracts, service specifications, and integration guides that serve as the primary handoff artifact between product and engineering, ensuring alignment before development begins. 

• Collaborating with platform engineering, security, and application product teams to define requirements, integration patterns, and governance standards for AI services built on or consumed through the platform. 

• Monitoring platform service performance metrics relevant to AI workloads — including API gateway throughput, service reliability, and usage telemetry — to ensure SLA commitments are met for dependent application teams. 

• Driving responsible AI standards at the platform layer — including data privacy controls, audit logging, and compliance guardrails — so that application teams inherit governance by default rather than solving it independently. 

• Developing the AI fluency of the platform product management team — building shared understanding of how AI workloads stress platform services differently, and ensuring PMs can critically evaluate requests from application teams building AI-powered features. 

• Building and maintaining key relationships with strategic partners and representing NiCE CXone as an industry thought leader at customer sites, trade shows, and industry events. 

• Partnering with sales and go-to-market teams to support opportunities, communicate platform capabilities, and respond to customer requirements and escalations. 

• Providing leadership through coaching, feedback, development goals, and performance management; responsible for recruiting and hiring within the department. 

• Reporting on performance metrics and adjusting priorities, staffing, and workloads to ensure efficient operation in alignment with departmental goals and objectives. 

To Land This Gig You'll Need: 

• Bachelor's Degree in Computer Science, Business Information Systems, Marketing, or similar field preferred, or equivalent experience; MBA would be a great addition. 

• 12+ years of product management experience with formal product management techniques, tools, and principles for planning and delivering software products and services. 

• Demonstrated experience delivering platform or infrastructure capabilities that enabled engineering teams to build and ship AI-powered products at scale. 

• Fluency in AI/ML concepts sufficient to evaluate how AI workloads impact platform services — including API design, security, scalability, and observability — without needing to write code. 

• Working knowledge of LLM and generative AI capabilities sufficient to anticipate platform needs — including API design considerations, cost attribution, rate limiting, and security implications of integrating AI services into a multi-tenant platform. 

• Experience defining and tracking success metrics for platform services supporting AI workloads — including adoption rates by application teams, time-to-integrate, SLA adherence, and reduction in duplicated AI infrastructure across product lines. 

• Experience with spec-first or API-first development practices, including authoring technical product specifications that bridge product intent and engineering implementation across a shared platform used by multiple application teams. 

• Demonstrated ability to lead through influence, present confidently to customers and co-workers, and engage effectively with technical resources — listening carefully, asking the right questions, and translating technical inputs into product decisions. 

• In-depth knowledge of agile & SCRUM development methodologies and experience with a SaaS product set. 

Experience Preferred: 

• Contact center experience with an emphasis on reporting, data usage, and analysis; familiarity with omnichannel and multichannel contact management. 

• Familiarity with conversational AI, virtual agents, or generative AI applications — particularly from the perspective of understanding what shared platform capabilities these features require to scale across a multi-tenant environment. 

• Understanding of AI governance considerations — including data privacy, audit logging, and regulatory requirements (GDPR, CCPA) — as they apply to building compliance capabilities into a shared platform used by multiple application teams. 

• Familiarity with agentic AI workflows and multi-step AI orchestration — to anticipate platform service requirements as application teams begin building autonomous AI-driven features on top of the platform. 

• Experience tracking how platform and infrastructure capabilities are evolving to support AI workloads in enterprise SaaS — including awareness of emerging patterns in multi-tenant AI governance, shared model serving, and developer experience for AI-enabled platforms.