Eaton's Corporate IT Organization is driving the adoption of innovation across Eaton by managing the development and execution of the enterprise AI strategy, roadmap, and investment priorities, ensuring alignment with broader organizational objectives. They will lead the creation and implementation of a comprehensive AI strategy that supports the company's vision and goals.
Requirements
- Deep understanding of AI/ML frameworks (e.g., PyTorch, TensorFlow), industrial data platforms (e.g., OSIsoft PI, Snowflake), and programming languages (Python, SQL).
- Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines) and edge deployment strategies.
- Proven success delivering AI-powered solutions at scale in manufacturing or industrial environments.
- Experience with AI applications such as predictive maintenance, defect detection, process optimization, and supply chain analytics.
Responsibilities
- Lead the adoption of generative AI, computer vision, predictive maintenance, and intelligent automation to optimize manufacturing processes.
- Build and lead a high-performing AI engineering team focused on scalable, production-grade solutions for industrial environments.
- Oversee the full AI/ML lifecycle: data ingestion from OT/IT systems, model development, deployment to edge/cloud, and performance monitoring.
- Champion MLOps best practices and cloud-native architectures (e.g., Azure ML, AWS SageMaker, Databricks) tailored for manufacturing use cases.
- Establish enterprise-wide AI governance, including ethical use, compliance with industrial standards, and risk mitigation.
- Develop policies and frameworks for responsible AI, model transparency, and bias mitigation in safety-critical environments.
- Evaluate emerging AI technologies and vendor partnerships to inform build-vs-buy decisions for manufacturing applications.
Other
- Minimum 10 years of experience in engineering or technology leadership, with a focus on AI, data science, or digital transformation.
- Minimum 2 years of enterprise IT leadership experience defining and executing AI strategies for a multi-billion-dollar industrial organization.
- Must be authorized to work in the U.S. without company sponsorship.
- Strong track record of influencing senior stakeholders and driving enterprise-wide transformation in manufacturing settings.
- Drive enterprise-wide education and change management to foster AI literacy and adoption across plants and engineering teams.
