Director of Artificial Intelligence
Leeds Professional Resources, Miami, FL, United States
Our client is seeking a senior technology leader to drive the organization’s next phase of
AI adoption and enterprise data platform modernization . This role will guide the strategy, development, and delivery of advanced analytics, machine learning, and automation initiatives designed to improve operational performance and unlock new business value.
The Director will oversee cross-functional teams responsible for data science, machine learning engineering, data architecture, and automation while partnering closely with executive leadership to integrate AI capabilities into core business operations. A major focus of the role will be advancing the company’s enterprise data platform, including modernization initiatives built around
Microsoft Fabric and modern lake house architecture .
Key Responsibilities
AI Strategy & Business Enablement Develop and lead a long-term strategy for AI and advanced analytics aligned with organizational goals. Identify high-value opportunities where machine learning and predictive analytics can improve revenue generation, forecasting, pricing strategy, and customer insights. Oversee the lifecycle of AI-driven solutions—from concept and experimentation through production deployment and ongoing optimization. Establish performance metrics and reporting frameworks that demonstrate the financial and operational impact of AI initiatives. Communicate strategy, risks, and progress to senior leadership.
Machine Learning & Advanced Analytics Guide teams responsible for developing and deploying production machine learning models and advanced analytical solutions. Establish best practices for
model development, testing, monitoring, and lifecycle management . Promote experimentation frameworks such as controlled testing and performance evaluation. Ensure that AI systems follow responsible AI principles, security standards, and regulatory requirements.
Intelligent Automation & Operational Transformation Identify internal processes that can benefit from automation powered by AI or intelligent agents. Lead implementation of automation capabilities that integrate with key business systems such as ERP, CRM, and HR platforms. Drive initiatives that reduce manual work, improve productivity, and streamline operational workflows. Track and report measurable ROI from automation initiatives.
Enterprise Data Platform & Architecture Lead the evolution of the organization’s enterprise data ecosystem, with a focus on modern cloud-based analytics architecture. Support initiatives related to: Lakehouse data architecture Real-time and batch data pipelines Data governance and lineage tracking Standardized metrics and semantic data models Integration of data engineering, analytics, and machine learning platforms Develop a roadmap to migrate legacy data infrastructure and reporting systems into a modern analytics platform with minimal disruption to business operations. Establish architectural standards, reusable data assets, and best practices across the data organization. Partner with security and infrastructure teams to ensure reliability, scalability, and cost efficiency.
Program Leadership & Execution Oversee large, cross-functional programs related to AI adoption and data platform modernization. Implement governance structures to manage priorities, budgets, delivery milestones, and risk management. Define clear success metrics and reporting frameworks for transformation initiatives. Manage relationships with external technology partners and consulting firms. Ensure projects are executed efficiently and deliver measurable business value.
Stakeholder Engagement Partner with leaders across operations, marketing, and technology to identify opportunities for data-driven innovation. Translate complex AI and data initiatives into clear business outcomes for executive stakeholders. Serve as a bridge between technical teams and operational leaders to ensure successful adoption of new capabilities. Help foster a culture of data-driven decision making throughout the organization.
Qualifications Required Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (advanced degree preferred). Extensive experience in AI, machine learning, or advanced analytics environments. Several years of leadership experience guiding technical teams and enterprise-scale initiatives. Demonstrated success delivering data or AI solutions that drive measurable business outcomes. Strong understanding of: Machine learning and advanced analytics Data engineering and platform architecture Cloud-based data environments Data governance and lifecycle management Automation technologies and AI-enabled workflows
Preferred Experience modernizing enterprise data platforms, particularly with
Microsoft Fabric or similar lakehouse-based analytics architectures . Background managing large technology transformation programs. Experience communicating technical strategy to senior leadership or board-level stakeholders. Familiarity with
generative AI, LLM applications, or AI-enabled automation platforms . Advanced business or leadership training (MBA or equivalent).
Reports to:
Chief Information Officer Team Leadership:
Oversees teams spanning data science, machine learning engineering, data engineering, data architecture, automation engineering, and technical program management.
AI adoption and enterprise data platform modernization . This role will guide the strategy, development, and delivery of advanced analytics, machine learning, and automation initiatives designed to improve operational performance and unlock new business value.
The Director will oversee cross-functional teams responsible for data science, machine learning engineering, data architecture, and automation while partnering closely with executive leadership to integrate AI capabilities into core business operations. A major focus of the role will be advancing the company’s enterprise data platform, including modernization initiatives built around
Microsoft Fabric and modern lake house architecture .
Key Responsibilities
AI Strategy & Business Enablement Develop and lead a long-term strategy for AI and advanced analytics aligned with organizational goals. Identify high-value opportunities where machine learning and predictive analytics can improve revenue generation, forecasting, pricing strategy, and customer insights. Oversee the lifecycle of AI-driven solutions—from concept and experimentation through production deployment and ongoing optimization. Establish performance metrics and reporting frameworks that demonstrate the financial and operational impact of AI initiatives. Communicate strategy, risks, and progress to senior leadership.
Machine Learning & Advanced Analytics Guide teams responsible for developing and deploying production machine learning models and advanced analytical solutions. Establish best practices for
model development, testing, monitoring, and lifecycle management . Promote experimentation frameworks such as controlled testing and performance evaluation. Ensure that AI systems follow responsible AI principles, security standards, and regulatory requirements.
Intelligent Automation & Operational Transformation Identify internal processes that can benefit from automation powered by AI or intelligent agents. Lead implementation of automation capabilities that integrate with key business systems such as ERP, CRM, and HR platforms. Drive initiatives that reduce manual work, improve productivity, and streamline operational workflows. Track and report measurable ROI from automation initiatives.
Enterprise Data Platform & Architecture Lead the evolution of the organization’s enterprise data ecosystem, with a focus on modern cloud-based analytics architecture. Support initiatives related to: Lakehouse data architecture Real-time and batch data pipelines Data governance and lineage tracking Standardized metrics and semantic data models Integration of data engineering, analytics, and machine learning platforms Develop a roadmap to migrate legacy data infrastructure and reporting systems into a modern analytics platform with minimal disruption to business operations. Establish architectural standards, reusable data assets, and best practices across the data organization. Partner with security and infrastructure teams to ensure reliability, scalability, and cost efficiency.
Program Leadership & Execution Oversee large, cross-functional programs related to AI adoption and data platform modernization. Implement governance structures to manage priorities, budgets, delivery milestones, and risk management. Define clear success metrics and reporting frameworks for transformation initiatives. Manage relationships with external technology partners and consulting firms. Ensure projects are executed efficiently and deliver measurable business value.
Stakeholder Engagement Partner with leaders across operations, marketing, and technology to identify opportunities for data-driven innovation. Translate complex AI and data initiatives into clear business outcomes for executive stakeholders. Serve as a bridge between technical teams and operational leaders to ensure successful adoption of new capabilities. Help foster a culture of data-driven decision making throughout the organization.
Qualifications Required Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (advanced degree preferred). Extensive experience in AI, machine learning, or advanced analytics environments. Several years of leadership experience guiding technical teams and enterprise-scale initiatives. Demonstrated success delivering data or AI solutions that drive measurable business outcomes. Strong understanding of: Machine learning and advanced analytics Data engineering and platform architecture Cloud-based data environments Data governance and lifecycle management Automation technologies and AI-enabled workflows
Preferred Experience modernizing enterprise data platforms, particularly with
Microsoft Fabric or similar lakehouse-based analytics architectures . Background managing large technology transformation programs. Experience communicating technical strategy to senior leadership or board-level stakeholders. Familiarity with
generative AI, LLM applications, or AI-enabled automation platforms . Advanced business or leadership training (MBA or equivalent).
Reports to:
Chief Information Officer Team Leadership:
Oversees teams spanning data science, machine learning engineering, data engineering, data architecture, automation engineering, and technical program management.