AI Enterprise Technology Leader

As a direct report to the President, Fluid Handling Systems and Chief Technology and AI Officer, the AI Enterprise Technology Leader will lead the definition and execution of the company’s enterprise AI strategy, with primary focus on embedding multi-agent AI systems into core value-chain functions: business acquisition (requirements analysis, cost estimation, and quoting), engineering design and validation, manufacturing processes, quality management, procurement, program management, account lifecycle management, and core finance functions. Own the detailed strategy, architecture, prioritization, vendor/build decisions, value measurement, and organizational change required to shift from AI experimentation to AI as the operating system of the business. Specifically, you will be expected to:

• Develop and maintain a prioritized, multi-year AI transformation roadmap that sequences the target functions by value, feasibility, data readiness, and risk, with clear ROI targets and stage-gate criteria.
• Design the technical and operating architecture for multi-agent systems (leverage data foundations, orchestration layer, integration with ERP/PLM/MES/QMS/CRM, human-in-the-loop controls, and governance) that can scale across quoting, engineering, quality, procurement, program management, and finance.
• Lead pilot-to-production transitions, including vendor selection, build-vs-buy decisions, internal capability building, and measurement of cycle-time, cost, quality, and risk outcomes.
• Drive the organizational and process redesign required so that AI agents become the primary doers of defined workflows rather than assistants bolted onto legacy processes; partner with function leaders on workforce transition and upskilling.
• Establish and operationalize enterprise AI governance in partnership with enterprise governance, cybersecurity, legal, compliance, data and architecture teams to ensure AI initiatives align with company policies, standards, and responsible AI practices.
• Drive execution discipline through value measurement, lifecycle management, transparency, adoption metrics, and operational controls that enable AI to scale responsibly across the enterprise., Additional Information: Cooper Standard is proud of its diverse workforce and committed to providing equal employment opportunities to applicants and employees without regard to race, color, religion, sex, national origin, genetic information, physical or mental disability, age, veteran or military status, or any other characteristic protected by applicable law. We are dedicated to creating an environment at work that not only values diversity but also encourages inclusion and a sense of belonging. We firmly believe that a diverse workplace fosters an environment where our employees can flourish and provide superior service to our customers. Because we recognize and value the range of ways in which people acquire experiences, whether personal, professional, or via education or volunteerism, we invite interested applicants to evaluate the key duties and requirements and apply for any opportunities that fit your experience and qualifications. Applicants with disabilities may be entitled to reasonable accommodations under the Americans with Disabilities Act, as well as certain state and/or local laws. If you believe you require such assistance to complete our online application or to participate in an interview, you (or someone on your behalf) may request assistance by emailing recruitment@cooperstandard.com with a description of the accommodation you seek. Application materials submitted to this email address will not be considered. Remote Status: Hybrid

• Demonstrated experience deploying agentic or generative AI systems at meaningful scale inside a complex manufacturing, industrial, or discrete-parts environment.
• Deep practical knowledge of enterprise systems integration (ERP, PLM, MES, QMS) and of building reliable RAG + agent orchestration layers on top of messy industrial data.
• Proven track record of delivering measurable productivity, cycle-time, or cost outcomes from AI (not just pilots); comfortable quantifying and defending ROI to a public-company board and CFO.
• Experience designing human-in-the-loop systems and AI governance frameworks.
• Technical fluency in modern LLM/agent stacks, MLOps, evaluation methods, and the practical limitations of current models, combined with business judgment about when not to automate.