DIR OPERATIONS

Job Description

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Job Title: DIR OPERATIONS

Posting Start Date: 6/8/26

At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.

Job Description:

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

AI, Data & Technology Experience

• 10+ years leading technology, digital, analytics, AI, or transformation initiatives in a complex enterprise environment.
• Experience in AI / GenAI strategy, solution design, deployment, scaling, and value realization.
• Strong understanding of data platforms, enterprise architecture, cloud platforms, APIs, model deployment, system integration, cybersecurity, and AI lifecycle management.
• Experience moving AI, analytics, optimization, automation, or software-enabled solutions from pilot into production.
• Ability to engage effectively with data scientists, software engineers, architects, IT leaders, automation engineers, and business stakeholders.

Manufacturing, Supply Chain & Operations Experience

• 10+ years in manufacturing, operations, supply chain, industrial engineering, quality, operational excellence, or plant transformation.
• Proven experience working in or directly supporting global manufacturing environments across multiple sites, regions, or business units.
• Strong understanding of plant operations, production systems, equipment performance, labor productivity, materials flow, quality systems, maintenance, planning, logistics, and cost drivers.
• Knowledge of lean manufacturing, continuous improvement, standard work, visual management, daily management, problem solving, kaizen, and performance management systems.
• Experience with core operations metrics such as OEE, yield, scrap, productivity, labor efficiency, inventory, delivery performance, quality, safety, and conversion cost.
• Familiarity with ERP, MES, QMS, CMMS, WMS, APS, SCADA, historian platforms, automation systems, and plant connectivity.

Preferred Qualifications

• Experience deploying AI, analytics, or digital manufacturing solutions across multiple plants or regions.
• Experience in industrial, automotive, electronics, aerospace, medical, or other complex manufacturing environments.
• Experience with computer vision, predictive maintenance, process optimization, quality analytics, production scheduling, demand / supply planning, or AI-enabled knowledge management.
• Experience connecting AI initiatives to financial outcomes including productivity, conversion cost, working capital, inventory, scrap, warranty, and cost of poor quality.
• Familiarity with Industry 4.0, smart factory, digital thread, digital twin, or connected factory strategies.
• Advanced degree in engineering, computer science, data science, operations management, supply chain, business, or a related field preferred.

Competencies

SET : Strategy, Execution, Talent (for managers)

Job Locations:

Posting City: #

Job Country: France

Travel Required: 50% to 75%

Requisition ID: 153588

Workplace Type: Remote

External Careers Page: Manufacturing

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