AI Enterprise Architect

Duties and Responsibilities: Serve as the enterprise technical leader for AI architecture and design, responsible for establishing standards and ensuring the scalability, security, and integrity of AI solutions across DFA. Partner closely with the enterprise product owner to translate business priorities into robust technical implementations and jointly deliver enterprise AI outcomes. Lead complex cross functional AI projects. Work independently to solve complex, high-risk issues and make architectural decisions with significant enterprise impact. Contribute to long-term AI strategy. Define enterprise AI architecture blueprint including Azure AI Foundry and integration patterns Ensure alignment with data, cloud, and security architecture across ERP and CRM systems (e.g., SAP, Salesforce, JDE) Evaluate AI/ML technologies aligned with enterprise standards Lead end-to-end design of AI solutions Translate business requirements into scalable technical designs Guide implementation teams on architecture adherence Embed responsible AI principles including transparency and traceability Ensure compliance with enterprise and regulatory standards Maintain architecture documentation Act as technical advisor across stakeholders Lead architecture reviews and design sessions Partner closely with the enterprise product owner as a joint leadership model to align business priorities with technical execution The requirements herein are intended to describe the general nature and level of work performed by employee, but is not a complete list of responsibilities, duties, and skills required. Other duties may be assigned as required Undergraduate degree in Computer Science, Engineering, Data Science, or related curriculum (or equivalent combination of education and experience) (Master’s preferred) 10+ years in solution architecture or engineering 7+ years in AI/ML environments Experience with cloud platforms and enterprise systems integration Experience delivering production AI solutions Strong understanding of distributed systems Experience with MLOps and governance frameworks preferred Deep expertise in AI/ML and GenAI architectures, including model deployment, integration, and lifecycle management Strong command of enterprise cloud, data, and integration architectures (APIs, event driven systems, microservices) Hands on understanding of MLOps, monitoring, scalability, and cost governance Advanced knowledge of enterprise security, privacy, and compliance as they apply to AI systems Proven ability to influence senior leaders and technical peers through expertise and credibility Exceptional ability to translate business strategy into durable technical architectures Comfortable operating with minimal direction in ambiguous, high impact environments Must be able to read, write and speak English Able to travel 15-25% of the time An Equal Opportunity Employer including Disabled/Veterans