The AI Solution Architect Lead will design scalable and high-performance AI solutions that align with the strategic objectives of the organization. This role is responsible for translating business requirements into technical architectures, ensuring that AI models and systems integrate seamlessly with existing technologies across OpCos. The Solution Architect will work closely with the AI Engineers, Data Scientists, and Business Analysts to create sustainable, adaptable, and efficient AI systems that drive business value

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Architectural Design: Develop and maintain the overall AI architecture, ensuring it is scalable, secure, and aligned with the organization’s business goals. Provide architectural blueprints and technical leadership to AI teams across HQ and OpCos.

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Solution Development: Design end-to-end AI solutions that integrate data processing, machine learning, and AI models into the existing business infrastructure, ensuring alignment with both technical and business requirements.

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Technology Evaluation: Stay up to date with emerging AI technologies and tools. Evaluate and recommend new tools and platforms that can enhance AI performance and scalability within the organization.

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Collaboration: Work closely with AI Engineers, Data Scientists, and Business Analysts to ensure that AI models are well-integrated into the business process and technical ecosystem, considering both local (OpCo) and global (HQ) needs.

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Technical Leadership: Provide technical guidance and mentorship to AI Engineers and Data Engineers. Ensure best practices for AI model design, deployment, and scalability are followed.

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System Integration: Ensure seamless integration of AI systems with other enterprise systems (e.g., CRM, ERP, marketing platforms). Oversee technical execution to ensure that the system design and AI solutions are properly implemented.

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Security and Compliance: Ensure that AI solutions meet the organization’s security and compliance requirements, including data governance, privacy regulations, and ethical AI guidelines.

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Performance Optimization: Continuously monitor the performance of AI systems and identify opportunities to optimize architecture and model efficiency.