Senior AI Architect

Key Responsibilities

• AI Architecture Leadership: Define and evolve Weyerhaeuser’s enterprise AI and agentic architecture to enable scalable, secure, and interoperable AI solutions across business domains.
• Establish standards for multi-agent ecosystems, generative AI reasoning pipelines, and MCP-based interoperability to support dynamic, context-aware AI applications that deliver measurable business outcomes.
• AI Platform and Framework Design: Architect and implement the foundational platform for Agentic and classic AI, encompassing model orchestration, retrieval-augmented generation (RAG), memory systems, and Agent-to-Agent (A2A) communication frameworks.
• Partner with business, data, product, and engineering teams (internal and external) to translate business opportunities into technical architectures that accelerate AI delivery.
• Collaborate with IT, cybersecurity, and enterprise architects to ensure AI systems integrate safely and sustainably within Weyerhaeuser’s technology ecosystem.
• Mentorship and Evangelism: Guide engineers, data scientists, and solution architects in applying architectural best practices for AI and MLOps. Evangelize AI innovation through internal knowledge sharing, cross-functional partnerships, and external collaborations.
• Responsible and Governed AI: Embed Weyerhaeuser’s Responsible AI principles — including safety, transparency, sustainability, and accountability — into every stage of the AI lifecycle. Bolster governance practices covering model lineage, monitoring, explainability, and continuous improvement.
• AI Systems Integration: Architect and oversee the integration of AI models, services, and agents into enterprise systems such as SAP, ServiceNow, Snowflake, and Azure, ensuring interoperability, reliability, and performance across applications, data, and workflows.
• Innovation and Scalability: Evaluate and prototype emerging AI technologies — including multi-agent systems, large language models, and generative AI platforms — to identify new opportunities for operational excellence and workforce augmentation.
• Standards, Tools, and Best Practices: Define and promote development standards, reusable components, and reference architectures that enable consistency, security, and speed across all AI initiatives. Champion modular, cloud-native, and API-driven design principles.
• Performance and Cost Optimization: Design architectures that balance compute efficiency, latency, and cost, ensuring AI systems deliver sustained business value at scale.

Job Information

Technology Primary Location: USA-WA-Seattle
Schedule: Full-time
Job Level: Individual Contributor
Job Type: Experienced
Education: Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s or PhD in AI, Data Science, or a similar discipline preferred.
Experience: 8+ years of experience in AI architecture, data science, or software engineering, including large-scale production deployments of machine learning, deep learning, or AI-driven systems in enterprise environments.