Senior AI Architect

Description:

Weyerhaeuser is a recognized leader in sustainable forestry and wood products, committed to innovation, operational excellence, and responsible stewardship. As Senior 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, data, and technology leaders - and external partners - to design and operationalize enterprise AI architectures built on governed, high-quality data. Your work will integrate AI models, services, and agents with technologies such as Microsoft Copilot, Azure, OpenAI, AWS, SAP, and Snowflake, ensuring alignment with Weyerhaeuser's Responsible AI and governance standards. Acting as a bridge between innovation and implementation, you'll enable scalable, trustworthy AI adoption across the enterprise - from supply chain optimization to geospatial and industrial automation.

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. This includes support for traditional machine learning and optimization models that remain critical for forecasting, control, and decision-support use cases. Ensure alignment with enterprise data platforms, governance standards, and Responsible AI principles while enabling experimentation, automation, and adaptive learning across ML models, generative systems, and intelligent agents.
• Cross-Functional Collaboration:
  
  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, Service Now, 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.

Qualifications:

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.

Architecture & Integration: Demonstrated ability to design cloud-native and edge AI architectures, integrating models, APIs, and agents into enterprise technology such as SAP, Service Now, Snowflake, AWS, and Azure. Proficiency with multi-agent orchestration using Model Context Protocol (MCP), Agent-to-Agent (A2A) interaction models, retrieval-augmented generation (RAG), vector databases, and context memory architectures.

Technical Expertise: Proven experience designing and implementing enterprise-grade AI platforms leveraging both classic ML…