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 ownership—from 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.