Chief Data/AI Officer- Enterprise AI

Riverside Research is evolving into a mission-aligned innovation organization - embracing a fail-fast, test-often culture that accelerates experimentation, learning and delivery of rigorous, operationally relevant solutions for the end users. Given this direction, there is critical need to establish and operationalize a shared AI platform that enables the organization to function as one integrated system, accelerating mission impact while eliminating duplication. Hence, the Chief AI officer is responsible for designing, building and governing the enterprise AI ecosystem - including shared platform, tooling, standards, and operating model - such that AI capabilities are developed once, reused broadly, and deployed at mission speed. This role does not centralize delivery ownership from business units. Instead, it ensures that how AI is built, integrated, and sustained underneath programs is unified, interoperable and scalable.

Enterprise AI Platform: Own and deliver the shared AI substrate that all programs build upon - data pipelines, model lifecycle management, simulation and testing environments, compute orchestration, deployment runtime.
• AI Architecture and Reuse: Define and enforce modular, composable architecture - standard interfaces and message schemas for sensors, models and effectors, reusable AI services, capability catalog (mission App Store). If capability exists anywhere in the enterprise, it is reused - not rebuilt.
• Adoption and Integration: Drive enterprise-wide adoption of AI tools and platform services per the Project Helix mission. Partner with BU leaders to integrate capabilities into programs, embed platform usage into program execution workflows, and establish "build-on-platform" as the default operating model.
• AI Governance: Lead an AI Governance function under CTO authority. Review and approve new AI development efforts, prevent duplication across business units, enforce standards, reuse, architectural compliance and rapidly adjudicate cross-BU conflicts
• Operational Credibility: Ensure all AI capabilities are validated and tested under real-world conditions, resilient to adversarial manipulation, measurable against key performance parameters, deployable and sustainable in operational environments where applicable
• AI Portfolio Alignment: Map AI capabilities to qualified pipeline and customer demand, track ROI of platform vs fragmented development and prioritize high-impact reusable capabilities

• 10-15+ years of progressive leadership in enterprise-scale AI/ML systems working to drive enterprise-wide adoption including embedding AI into workflows, training and enabling engineering teams, and converting pilots into production systems
• Experience driving organizational changes and building consensus among diverse stakeholders across multiple business units/organizations without direct formal authority
• Experience managing portfolios tied to business outcomes
• Demonstrated success enabling multiple teams to build on a shared AI platform
• Experience standing up and leading platform adoption mandates and technical governance boards
• Deep understanding of data engineering, distributed systems, MLOps pipelines and cloud architectures
• Proven ability to define and enforce APIs, schemas, and integrated standards
• Understanding of modular software and microservices architecture
• Master's degree with over 15 years of relevant experience in computer science, engineering, data science, or other STEM disciplines. Additional years of relevant experience will be considered. Operational experience working with end-users to field capabilities will be considered.
• US citizenship with an active security clearance.