Job Description Summary

The Director of AI Strategy & Transformation for Wind Engineering will establish and lead the application of Artificial Intelligence (AI) as a scalable and value-generating capability across the Wind Engineering organization. This role is accountable for defining the AI vision and roadmap, building the organizational and technical systems required to scale, and ensuring AI initiatives deliver measurable business outcomes.

As a newly created leadership role, this position requires a leader who can operate effectively in uncertainty, shape strategy while driving execution, and build durable structures—including governance, operating models, and partnerships—that enable sustained impact.

Job Description

Key Responsibilities:

AI Strategy, Vision & Value Delivery

• Define and own the AI strategy and multi-year roadmap for Wind Engineering, aligned with business priorities and enterprise AI direction.
• Identify, prioritize, and sequence high-value AI use cases across design, analysis, validation, manufacturing support, operations, reliability, and lifecycle optimization.
• Establish clear value hypotheses, success metrics, and ROI tracking for AI initiatives.
• Ensure balance between rapid experimentation and development of scalable, repeatable capabilities.

Scalable Systems, Platforms & Governance

• Design and implement AI systems that scale, including standards for data, models, tooling, deployment, and lifecycle management.
• Establish and lead AI governance for Wind Engineering, including:
  • Model risk management and validation
  • Data quality, lineage, and access standards
  • Responsible AI, safety, and regulatory compliance
  • Decision rights and investment prioritization
• Partner with Digital, IT, and Data leaders to align on platform strategy, architecture, and MLOps practices.
• Ensure AI solutions are maintainable, auditable, and reusable across products and teams.

Organizational Design & Talent Leadership

• Define the AI operating model for Wind Engineering (centralized, federated, or hybrid), including roles, interfaces, and engagement models.
• Build, lead, and develop a high-performing team of AI engineers, data scientists, and technical leaders within this operating model.
• Define capability requirements, skill profiles, and career paths for AI-enabled engineering roles.
• Lead hiring, onboarding, and succession planning for critical AI leadership and technical roles that would include direct and indirect reporting lines.

Adoption, Change & Engineering Integration

• Drive broad adoption of AI within engineering, embedding AI tools, workflows, and decision-support into standard engineering processes.
• Lead identification of core AI skillsets required across the entire Wind Engineering team. Incorporate these into competency models and work to develop and institute training as required.
• Partner with engineering leaders to integrate AI into design reviews, validation workflows, and operational decision-making.
• Lead change management efforts, including training, communications, and communities of practice, to increase AI fluency and trust.
• Establish feedback loops to continuously improve usability, effectiveness, and adoption.

External Partnerships & Ecosystem Development

• Develop and manage strategic external partnerships with AI technology providers, software vendors, startups, universities, and research institutions.
• Evaluate when to build, buy, or partner to accelerate capability development and value realization.
• Structure and govern partnerships to ensure IP protection, scalability, security, and long-term value.
• Represent Wind Engineering in external forums and collaborations related to AI and advanced engineering methods.

Cross-Business Collaboration

• Serve as a partner to Product Management, Supply Chain, Services, Commercial, and Enterprise Digital teams to ensure AI initiatives deliver end-to-end business impact.
• Align Wind Engineering AI efforts with enterprise AI standards, platforms, and investments, influencing direction where needed.
• Enable reuse and scaling of AI solutions across functions, regions, and product lines.

Leadership in Ambiguity

• Translate ill-defined problems and emerging opportunities into clear strategies, executable plans, and scalable solutions.
• Make informed tradeoffs across speed, risk, technical depth, and business value.
• Set direction and maintain momentum in a rapidly evolving technology and business landscape.

Qualifications & Experience

Required

• Bachelor’s degree in Engineering, Computer Science, Applied Mathematics, or related field (advanced degree strongly preferred).

Preferred

• Extensive experience in engineering leadership, digital transformation, or advanced analytics/AI within a complex industrial environment.
• Proven ability to design and lead governance, operating models, and scalable systems.
• Experience building and leading multidisciplinary teams.
• Strong executive communication and stakeholder management skills.
• Demonstrated success delivering AI-enabled business outcomes at scale, not just pilots or proofs of concept.
• Experience in wind energy, power generation, aerospace, or similarly regulated, safety-critical industries.
• Experience managing external partnerships and joint development initiatives.
• Familiarity with enterprise data platforms, cloud-native AI solutions, and MLOps frameworks.
• Experience operating in matrixed, global organizations.

Critical Success Factors

• Ability to create structure and momentum in ambiguity.
• Credibility with senior engineering, digital, and business leaders.
• Strong systems thinking—connecting data, models, people, and processes into scalable capability.
• Results orientation with a disciplined approach to governance and risk.

Why This Role Matters

This Director role will define how AI becomes a core, trusted, and scalable capability within Wind Engineering, enabling faster innovation, improved product performance, reduced risk, and differentiated customer value. The decisions made in this role will shape engineering practices for years to come.

For candidates applying to a U.S. based position, the pay range for this position is between $220,000 and $300,000. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.

 

 

For candidates applying to a U.S. based position only:

 

 

Bonus eligibility: discretionary annual bonus.

 

 

This posting is expected to remain open for at least seven days after it was posted on May 18, 2026.

 

 

Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity Executive Services. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off, and the Restoration Plan, a nonqualified plan with company credits on eligible pay above IRS limits.

 

 

GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.