Governance & AI Leader

The Governance & Responsible AI Leader is accountable for defining and enforcing the standards that govern safe, compliant, and responsible use of AI across Wind Engineering. This role manages technical and non-technical risk, including cybersecurity, intellectual property protection, regulatory compliance, and Responsible AI practices. The AI Governance Leader ensures that AI systems can be trusted, audited, and sustained over time, and operates independently from solution delivery to provide objective, credible oversight.

Reporting to the Director – AI Strategy & Transformation, the ideal candidate is a risk-oriented leader with a strong background in technology governance, compliance, or legal/regulatory frameworks who understands both the promise and the peril of AI deployment in a complex industrial engineering environment.

• 1. Define and Maintain the Wind Engineering AI Governance Framework Design, publish, and maintain a comprehensive AI governance framework covering risk management, compliance, cybersecurity, data standards, intellectual property, and Responsible AI. Establish clear decision rights, approval authorities, and escalation paths for AI deployments across Wind Engineering. Translate enterprise and regulatory requirements into practical governance standards that engineering teams can understand and apply.
  
  Ensure the governance framework evolves in pace with new AI capabilities, technologies, regulatory developments, and lessons learned from deployed solutions. Maintain a Wind Engineering AI governance register that tracks active policies, standards, waivers, and compliance status across the portfolio.
• 2. Establish and Enforce Responsible AI Policies Define Wind Engineering's Responsible AI principles, covering safety, transparency, accountability, explainability, and human oversight. Establish policies for how AI systems should behave in high-consequence or safety-critical engineering decisions, including clear human-in-the-loop requirements. Develop standards for data provenance, bias evaluation, and performance monitoring. Ensure that AI usage policies reflect current regulatory expectations and GE Vernova enterprise standards.
  
  Proactively monitor the external AI regulatory landscape and assess impact on Wind Engineering AI programs.
• 3. Oversee Risk Management, Validation, and Auditability Define risk tiers and corresponding validation requirements for AI solutions deployed across Wind Engineering. Establish requirements for documentation, performance monitoring, drift detection, and lifecycle management across deployed AI solutions. Ensure AI models used in engineering decisions are auditable, reproducible, and supported by appropriate documentation for quality management system (QMS) integration. Partner with Senior AI Architects to ensure that technical solutions are designed with auditability, traceability, and maintainability as first-order requirements.
• 4. Govern Cybersecurity, IP Protection, and Data Privacy for AI Partner with Product Security to define cybersecurity standards specific to AI systems, including model security, API access controls, adversarial attack awareness, and supply chain risk. Establish IP protection guidelines covering the use of third-party AI tools, vendor-hosted models, open-source AI components, and data shared with external AI platforms. Partner with Legal and IP Council teams to ensure AI data practices meet applicable privacy requirements, including data minimization, access controls, and residency standards.
  
  Evaluate new AI tools and vendors through a security and IP lens before introduction into Wind Engineering workflows. Develop guidance for the safe use of generative AI tools, including Git Hub Copilot, large language models, and AI-assisted design tools, in engineering contexts involving proprietary data.
• 5. Provide Governance Approval and Oversight for AI Deployments Operate as the governance checkpoint in the AI portfolio stage-gate process, providing structured review and approval at key maturity transitions (POC to MVP, MVP to PPS). Evaluate AI initiatives for risk profile, compliance readiness, data governance posture, security, and Responsible AI alignment before production deployment. Issue governance approvals, conditional approvals, or escalations based on risk level and policy alignment.
  
  Maintain a clear record of governance decisions, conditions, and exceptions to enable audit readiness and accountability. Ensure that AI deployments do not proceed to production scale without satisfying defined governance criteria.
• 6. Partner with Legal, Security, Privacy, and Compliance Teams Serve as the primary point of contact between Wind Engineering AI programs and enterprise Legal, Security, Privacy, Compliance, and Regulatory teams. Translate enterprise AI policies into Wind Engineering-specific requirements and ensure they are embedded into AI initiatives and Engineering design processes. Ensure that AI-related contractual…