Key Responsibilities

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 GitHub 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 obligations, vendor agreements, and data sharing arrangements are reviewed for governance risk.
• Represent Wind Engineering AI governance posture in enterprise governance forums and audits.
• Build strong working relationships with GE Vernova enterprise compliance, risk, and digital security teams to stay aligned with evolving requirements.

Required Qualifications

• Bachelor’s degree in engineering, Computer Science, or a related technical or regulatory field.
• Significant experience in technology governance, risk management, regulatory compliance, cybersecurity, or AI/data ethics in a complex technical organization.
• Demonstrated ability to develop, implement, and maintain governance frameworks in a matrixed, fast-moving environment.
• Practical understanding of AI, machine learning, and generative AI technologies and the specific risks they introduce in industrial or engineering contexts.
• Experience partnering with Legal, Security, Privacy, and Compliance functions to develop enforceable policies that teams can practically apply.
• Strong stakeholder engagement skills, with the ability to influence without direct authority across engineering, digital, legal, and leadership teams.
• Excellent written communication skills, with the ability to translate complex regulatory and risk concepts into clear, actionable standards.

Preferred Candidate Characteristics

• Familiarity with AI regulatory frameworks such as the EU AI Act, NIST AI Risk Management Framework, ISO/IEC 42001, or equivalent standards.
• Experience with model risk management in regulated industries (e.g., financial services, aerospace, energy, or healthcare).
• Knowledge of cybersecurity frameworks applicable to AI systems, including OWASP ML Security Top 10 or MITRE ATLAS.
• Understanding of intellectual property risk in AI contexts, including training data rights, model ownership, and vendor IP exposure.
• Experience supporting quality management systems (QMS) with AI-specific documentation requirements or traceability standards.
• Comfortable operating in ambiguity and building structured governance approaches in environments where AI standards are still emerging.
• Strong integrity and independence mindset, willing to raise concerns, issue escalations, and hold firm on governance standards even under delivery pressure.