AI Platform Technical Owner 

Hands-on AI Platform Technical Owner to architect, govern, and evolve their enterprise AI infrastructure. This is not a feature-level product role. This position owns the reusable AI and data platform foundation that integrates with publishing systems, member behavioral data, and multiple active AI initiatives. The ideal candidate has built production-grade AI platforms at enterprise scale and can balance architecture, governance, scalability, cost management, and cross-functional alignment.

Responsibilities

• Define and own the enterprise AI and data platform strategy aligned to business and technical goals.

• Establish and document evolving AI and data architecture standards across initiatives.

• Lead design and productionization of scalable AI platform components, not experimental prototypes.

• Define integration patterns, APIs, deployment standards, and production-readiness criteria.

• Establish reusable ModelOps processes including versioning, CI/CD, evaluation, monitoring, and lifecycle management.

• Implement AI observability including drift detection, latency monitoring, throughput tracking, and incident response targets.

• Build AI FinOps guardrails including usage monitoring, cost optimization, and budget alignment.

• Integrate AI capabilities with publishing systems, proprietary knowledge assets, and member data ecosystems.

• Design scalable AI infrastructure that supports personalization, semantic enrichment, and knowledge-driven applications.

• Partner with internal engineering, product, content, data, and security teams to translate requirements into technical roadmaps.

• Work closely with the AI governance committee to ensure AI development is monitored, documented, and aligned with enterprise policies.

• Coordinate external consulting partners and guide build-vs-buy decisions while preventing vendor lock-in.

• Drive adoption through documentation, enablement materials, and reusable platform standards.

• Define and track platform KPIs across reliability, adoption, performance, and cost.

Requirements

• Proven experience architecting and delivering production-grade enterprise AI platforms (not just POCs).

• Strong hands-on experience with large language models in real-world environments.

• Experience designing and deploying RAG systems, embeddings pipelines, and semantic retrieval architectures.

• Experience working with vector and graph databases.

• Strong background in scalable AI infrastructure, distributed systems, and cloud-native deployment.

• Experience implementing ModelOps and lifecycle governance for AI systems.

• Experience with AI observability, monitoring, and cost management.

• Familiarity with Responsible AI and enterprise governance frameworks.

• Experience collaborating across engineering, product, data, and executive stakeholders in complex organizations.

• Ability to operate in ambiguity and define structured technical roadmaps.

• Cloud deployment experience (Microsoft Azure preferred, but strong cloud experience required).