Principal Enterprise Architect

As a Principal Enterprise Architect at Kimberly-Clark USA, LLC, you will play a key role in designing and implementing standards and data frameworks that support the development, deployment, and scaling of AI and ML models across the enterprise.

The scope of the enterprise architecture process involves the interpretation of business goals, drivers, and strategies, the assessment of the current capabilities of the people, processes, information, and technology of the enterprise, and the determination of how these relate to one another and to the external environment.

The process supports the formation of the roadmaps, standards, and guiding principles required to define, assure, and govern the required evolution and the transitional processes that facilitate predictable transition to the intended state through information-enabled change in the organization's structure, business processes, information systems, and infrastructure.

This role sits at the intersection of AI technology, data engineering, and strategic business initiatives. The Enterprise Data Architect will ensure the company's data architecture is capable of supporting cutting-edge AI and ML technologies while ensuring scalability, security, and alignment with business goals.

Key Responsibilities:

• Own the North Star Architecture for AI/ML and Generative AI, aligning to other technical and functional target architectures and driving technology adoption of the three-year roadmap
• Define Enterprise Data Architecture: Design scalable, secure, and efficient data architectures that enable the integration and use of AI, ML, and Generative AI
• Research and Development: Lead R&D initiatives to explore innovative AI/ML use cases, such as predictive analytics, automation, or natural language processing (NLP)
• Data Security: Architect solutions that adhere to strict data privacy and security protocols, especially in sensitive environments
• Model Lifecycle Management: Collaborate with data scientists and ML engineers to develop a robust framework for model versioning, testing, and deployment across environments
• Data Infrastructure: Collaborate with the data platform team to design the underlying infrastructure to support large-scale AI/ML workloads, including storage, pipelines, and real-time processing capabilities
• Data Collection and Integration: Ensure the smooth integration of data from multiple sources (structured and unstructured) into AI/ML models, including setting up ETL/ELT pipelines
• Training and Mentorship: Lead and mentor teams in AI, ML, and data engineering to build skills within the organization

This role requires a strong understanding of AI, ML, and data engineering principles, as well as excellent leadership and collaboration skills. If you are passionate about driving innovation and excellence in data architecture, we encourage you to apply.