Director, AI Platform Engineering - Manufacturing

Cargill seeks a Director, AI Platform to lead the strategy, design, and delivery of an enterprise-scale AI platform powering its global manufacturing operations. In this role, you will define the platform roadmap, enabling predictive maintenance, quality optimization, and supply chain efficiency across complex production environments. Partnering with manufacturing, IT, and data science teams, you'll industrialize AI solutions through robust data pipelines, MLOps, governance, and secure cloud infrastructure. You will build and mentor a high-performing team, championing Cargill's culture of integrity, safety, inclusion, and continuous learning while delivering measurable business impact and supporting global food security and sustainability.

Responsibilities

• Lead the vision, strategy, and roadmap for Cargill's AI platform to support global manufacturing operations
• Define and prioritize AI platform capabilities enabling predictive maintenance, quality optimization, and supply chain efficiency
• Partner with manufacturing, IT, and data teams to identify high value AI use cases and scale solutions across plants
• Oversee architecture, data pipelines, MLOps, and governance to ensure secure, reliable, and compliant AI services
• Establish standards for model lifecycle management, monitoring, and performance in production environments
• Build and manage a high performing AI platform team, fostering innovation, experimentation, and continuous learning
• Collaborate with cybersecurity and compliance teams to maintain data privacy, security, and regulatory alignment
• Manage vendor relationships, cloud costs, and technology selection for AI and data infrastructure
• Define KPIs, track business impact, and communicate outcomes to executive stakeholders
• Champion a culture of safety, integrity, and inclusive collaboration across AI initiatives

Required Skills

• AI/ML platform architecture
• Cloud infrastructure (AWS, Azure, or GCP)
• Data engineering & pipelines (SQL, Python, Spark, ETL)
• MLOps tools and practices (CI/CD, model deployment, monitoring)
• Data governance, security, and compliance
• Microservices and API design
• Industrial/Manufacturing analytics (OEE, predictive maintenance, quality)
• Team leadership and cross-functional collaboration
• Product management and roadmap planning
• Cost optimization and vendor management