Principle AI Architect

Weyerhaeuser is a recognized leader in sustainable forestry and wood products, committed to innovation, operational excellence, and responsible stewardship. As Principal AI Architect, you will define and evangelize AI architectures that power Weyerhaeuser’s digital transformation. Spanning traditional machine learning, generative AI, and agentic AI, this role ensures solutions are scalable, secure, and responsible — driving measurable business value across our timberlands, wood products, and corporate functions. You will partner with business, product, data, and technology leaders to define the architectural foundation for AI across Weyerhaeuser. Working at the intersection of strategy and engineering, you will establish reference architectures, design patterns, and technical standards that enable teams to rapidly build secure, scalable, and reusable AI capabilities. Your work will shape how AI is integrated into business workflows, enterprise platforms, and operational systems accelerating innovation while ensuring solutions remain governable, resilient, and aligned with enterprise standards. You'll evaluate and guide the adoption of modern AI technologies including large language models, intelligent agents, cloud AI services, enterprise data platforms, and emerging interoperability standards while selecting the right technologies for the problem rather than building around any single vendor or framework. Key Responsibilities - Enterprise AI Architecture - Define and evolve Weyerhaeuser's enterprise AI architecture, reference architectures, and technical standards. Establish scalable, secure, and reusable architectural patterns that enable AI to become a core enterprise capability while ensuring interoperability, governance, and long-term maintainability. - AI Platform Strategy - Define the architectural foundation for enterprise AI platforms supporting machine learning, generative AI, intelligent agents, and optimization. Guide technology strategy, platform evolution, and the adoption of emerging AI capabilities while balancing innovation, operational maturity, and business value. - Technical Leadership - Provide technical leadership across the enterprise through architecture reviews, mentoring, engineering standards, and reusable design patterns. Establish engineering standards, reusable design patterns, architecture reviews, and technical guidance across the engineering teams for AI solutions. - Enterprise Collaboration - Partner with business, product, engineering, data, cybersecurity, and enterprise architecture teams to translate strategic business opportunities into technical solutions. Drive alignment across organizations to ensure AI capabilities integrate seamlessly into enterprise platforms and business workflows. - Responsible AI & Operational Excellence - Embed Responsible AI principles throughout the AI lifecycle by defining architectures that emphasize security, governance, transparency, observability, performance, reliability, and cost efficiency. Ensure AI solutions are designed for enterprise-scale operations and deliver measurable business outcomes.