Director, Responsible AI & Emerging Technology

AI Strategy Roadmap & Value Delivery

• Define and own the enterprise AI and emerging technology roadmap aligned to business strategy and manufacturing priorities (e.g. productivity quality cost safety resilience).
• Identify prioritize and sequence AI use cases based on value feasibility risk and scalability moving the organization from pilots to sustained production outcomes.
• Partner with Operations Engineering Supply Chain Quality and Corporate functions to co-create AI solutions that deliver measurable business impact.
• Establish patterns and reference architectures for repeatable scalable AI solutions across plants regions and business units.
• Oversee delivery of AI initiatives through clear milestones success metrics and lifecycle ownershipfrom ideation through decommissioning.

Responsible AI Governance & Risk Management

• Architect and operationalize Responsible AI governance frameworks covering the full AI lifecycle aligned with regulations and standards (e.g. EU AI Act NIST AI RMF ISO/IEC 42001).
• Design tiered AI risk classification models to guide approval paths controls and monitoring requirements based on use-case criticality.
• Chair or co-lead cross-functional AI governance councils partnering with Risk Security Data and Business leaders to evaluate higher-risk AI deployments.
• Ensure transparency traceability and accountability in AI systems including documentation model lineage and decision explainability appropriate to risk level.
• Serve as a trusted advisor to executives and boards on AI risk posture regulatory readiness and maturity progression.

AI Security & Trustworthy AI Engineering

• Build and lead AI-focused security practices addressing adversarial ML model integrity data provenance and secure deployment.
• Conduct AI threat modeling and risk assessments across models data pipelines vendors and deployment environments.
• Develop and execute AI red-teaming and adversarial testing programs (evasion poisoning model extraction prompt injection misuse scenarios).
• Define and enforce secure-by-design AI patterns embedding governance and security controls into MLOps/LLMOps pipelines and CI/CD workflows.
• Partner with Cybersecurity and Incident Response teams to integrate AI-specific risks signals and playbooks into enterprise security operations.

Leadership Enablement & Change

• Build and mentor multidisciplinary teams spanning AI engineering governance and security.
• Drive organizational AI maturity through training enablement and change management for both technical and non-technical stakeholders.
• Establish clear communication and reporting to demonstrate AI value risk management and compliance to leadership auditors and external stakeholders.
• Act as a visible thought leader on AI emerging technology and Responsible AI practices within the enterprise.

AI Platforms MLOps & Industrialization

• Guide the design and evolution of enterprise AI platforms
• Establish standards for MLOps/LLMOps enabling reliable deployment monitoring rollback and continuous improvement of AI systems.
• Implement monitoring for model performance drift bias/fairness robustness and misuse coordinating remediation where thresholds are exceeded.
• Ensure AI solutions are production-ready scalable and supportable within a global manufacturing environment.