Director Artificial Intelligence & Automation

Sonepar•Broomfield, CO

About The Position

Sonepar Management Group (SMG) supports our Sonepar brands (i.e. operating companies) in the US through a shared services model. These services include, but are not limited to: human resources, finance, digital enterprise, supply chain, vendor relations, marketing, legal, and communications. The SMG teams enable our brands to do business in their local regions while taking advantage of the scale and collective resources of a global enterprise.SMG fosters an inclusive and supportive culture. We offer leadership and development programs to help you reach your career goals. Our associates share in our collective achievements, and we firmly believe that Sonepar is “Powered by Difference.” By driving technology and innovation, enabling paths to success, and caring about our people and their families, we have built a workplace where you can build a fulfilling career. The Director Artificial Intelligence & Automation is responsible for leading Sonepar North America's Artificial Intelligence and Automation delivery capabilities and executing the enterprise AI strategy. This role oversees the design, development, deployment, operation, and continuous improvement of scalable AI, Generative AI, agentic workflow, and intelligent automation solutions that drive measurable business outcomes. Working closely with AI Product Management, Data & Analytics, Enterprise Architecture, Cybersecurity, and application development teams, this leader delivers secure, reliable, and reusable AI capabilities while ensuring operational excellence, governance, and responsible AI practices. The Director will play a critical role in shaping Sonepar's future technology landscape by identifying emerging technologies and transforming innovation into business value at scale.

Requirements

10+ years of progressive leadership experience in information technology, software engineering, AI engineering, automation, data and analytics, digital platforms, or related technology disciplines.
5+ years leading enterprise-scale AI, automation, advanced analytics, technology delivery, digital product engineering, or platform teams in complex organizations.
5+ years of project and program leadership experience utilizing Agile, DevOps, and modern software delivery methodologies.
Experience with enterprise data governance, cybersecurity, privacy, and risk management practices.
Proven track record deploying production-grade AI, automation, workflow, analytics, or software solutions that deliver measurable business value.
Experience establishing engineering standards, delivery methodologies, DevOps/MLOps practices, testing frameworks, monitoring capabilities, and support models.
Demonstrated success leading technical teams, managing vendors, and coordinating cross-functional delivery across architecture, data, security, infrastructure, and application teams.
Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Business, or a related field.
Equivalent combination of education and professional experience may be considered.

Nice To Haves

Experience in wholesale distribution, industrial distribution, manufacturing, supply chain, logistics, or related industries.
Experience scaling enterprise Generative AI and intelligent automation capabilities.
Experience supporting large, complex, multi-brand organizations.
Master's degree in Business Administration, Technology, Data Science, Analytics, Engineering, or a related discipline.
Professional certifications in AI, Cloud Computing, Automation, Agile Delivery, DevOps/MLOps, Product Management, or Enterprise Architecture.

Responsibilities

Lead execution of Sonepar's enterprise AI and automation portfolio, ensuring approved initiatives are delivered on time, within budget, and aligned with business objectives.
Drive successful deployment of Generative AI, machine learning, automation, and intelligent workflow solutions that generate measurable business impact.
Establish delivery plans, resource requirements, and success metrics for AI and automation initiatives.
Monitor value realization, adoption, performance, and operational outcomes across the portfolio.
Establish and lead AI Engineering, Automation Engineering, DevOps/MLOps, testing, release management, monitoring, and production support capabilities.
Ensure enterprise AI solutions are scalable, secure, reliable, and operationally sustainable.
Implement engineering best practices that support consistent deployment, monitoring, maintenance, and continuous improvement of AI-powered solutions.
Develop operational readiness standards for production-grade AI and automation applications.
Define and govern enterprise AI delivery standards, architecture patterns, reusable components, development frameworks, and implementation methodologies.
Establish standards for prompt engineering, AI agents, integration patterns, automation frameworks, documentation, and solution delivery.
Partner with Enterprise Architecture teams to ensure consistency, scalability, interoperability, and alignment with technology roadmaps.
Promote reuse and standardization to accelerate delivery and reduce technical complexity.
Drive continuous enhancement of AI delivery practices, development methodologies, testing disciplines, deployment strategies, and operational monitoring capabilities.
Evaluate emerging AI technologies, tools, platforms, and industry trends to identify opportunities that enhance business performance.
Establish processes for measuring solution performance, adoption, business value, and operational effectiveness.
Foster a culture of innovation, experimentation, learning, and continuous improvement.
Build, lead, and develop a high-performing team of AI engineers, automation engineers, solution architects, technical specialists, and delivery partners.
Create a culture focused on accountability, innovation, collaboration, and continuous learning.
Mentor and coach associates while developing future technical leaders and delivery capabilities.
Manage external vendors, systems integrators, and strategic partners to ensure successful delivery outcomes.
Implement Responsible AI practices, model governance, security controls, privacy requirements, and compliance standards across the AI lifecycle.
Partner with Cybersecurity, Legal, Risk Management, Compliance, Data Governance, and Enterprise Architecture teams to mitigate operational and regulatory risks.
Ensure AI solutions align with enterprise governance standards and ethical AI principles.
Maintain oversight of model performance, lifecycle management, and operational controls.