Job Overview

This role leads the strategy, deployment, and scaling of enterprise AI solutions across global manufacturing, supply chain, and operations functions. The position combines deep AI technical fluency with strong manufacturing and operations leadership to translate complex operational challenges into production-grade, scalable AI capabilities embedded in plant, quality, maintenance, planning, and enterprise workflows. The role ensures AI initiatives move beyond experimentation into governed, reliable solutions that improve productivity, quality, delivery, cost, safety, inventory, and decision-making.

Job Requirements

AI Strategy & Operations Use Case Framing 

• Define and drive the global AI for Operations roadmap aligned with manufacturing strategy, productivity goals, digital maturity, and business priorities. 
• Identify, prioritize, and frame high-value AI use cases across manufacturing, supply chain, quality, maintenance, engineering, procurement, and operations planning. 
• Translate operational challenges into scalable AI solution designs using advanced analytics, machine learning, optimization, computer vision, automation, and generative AI where relevant. 
• Tie AI initiatives to measurable outcomes such as OEE, yield, scrap, labor productivity, schedule adherence, inventory, quality, safety, delivery, and cost reduction. 
• Continuously assess emerging AI technologies and determine their practical relevance for industrial operations. 

Manufacturing & Operations Transformation 

• Partner with global, regional, and plant operations leaders to embed AI into core operating processes, daily management systems, and frontline workflows. 
• Lead AI-enabled transformation across production, maintenance, quality, materials, planning, logistics, and continuous improvement. 
• Improve shop-floor and supply-chain decision-making through AI-driven insights, alerts, recommendations, and automation. 
• Integrate AI into lean operating systems, standard work, visual management, problem solving, kaizen, tiered management, and performance routines. 
• Convert isolated pilots into repeatable, scalable operating models across plants, regions, and business units. 

AI Solution Architecture & Deployment 

• Guide end-to-end AI solution architecture across data, models, infrastructure, applications, enterprise systems, and operational workflows. 
• Oversee production deployment of AI solutions, ensuring robustness, scalability, security, reliability, maintainability, and adoption. 
• Partner with IT, data engineering, automation, cybersecurity, enterprise architecture, and platform teams to ensure infrastructure supports AI at scale. 
• Define reusable patterns, reference architectures, deployment playbooks, and governance practices for manufacturing and operations use cases. 
• Ensure effective integration with ERP, MES, QMS, CMMS, WMS, planning systems, automation platforms, historians, and plant data systems. 

Data, Governance & Value Delivery 

• Champion reliable operations data foundations, including data availability, quality, lineage, industrial data models, sensor data, machine data, process data, and transactional data. 
• Lead AI initiatives from concept through pilot, deployment, scaling, and sustainment using disciplined execution, agile delivery, value tracking, and adoption metrics. 
• Establish prioritization based on business value, feasibility, scalability, data readiness, operational urgency, and financial impact. 
• Define AI lifecycle governance, monitoring, privacy, cybersecurity, responsible AI, human oversight, explainability, auditability, and escalation paths. 
• Partner with legal, risk, compliance, cybersecurity, finance, HR, engineering, IT, and operations leaders to balance speed, innovation, control, and enterprise adoption. 

Capability Building & Change Leadership 

• Build AI capability across operations leaders, engineers, CI teams, plant teams, planners, quality teams, and functional experts. 
• Define training, standards, playbooks, communities of practice, and coaching models that help operations leaders identify, sponsor, deploy, and scale AI-enabled improvements. 
• Create a controlled ecosystem that balances central AI expertise with distributed operational ownership. 
• Drive adoption through usability, workflow integration, change management, ownership, sustainment, and performance management. 

What your background should look like

Competencies