Senior AI Solutions Architect - Wind Engineering

About the role

The AI Solutions Architect is a subsystem-aligned leader who identifies, designs, deploys and sustains AI solutions that deliver measurable value within a specific Wind Engineering domain. Acting as a bridge between AI capabilities and engineering competencies, the role translates business and technical challenges into practical AI-enabled solutions that improve productivity, quality, speed and engineering outcomes. Reporting to a Subsystem team with a dotted-line technical relationship to a Senior AI Architect, the architect works with engineers, technical leaders, product owners and process leaders to embed AI into daily engineering workflows.

Key responsibilities

• Identify high-value AI use cases with subsystem leadership, build an AI opportunity roadmap and evaluate use cases for feasibility, data availability, value and complexity.
• Design and deploy AI solutions using approved architectures and patterns, including agentic workflows, copilots, automation, predictive models and knowledge systems, and take part in architecture reviews.
• Integrate AI into engineering tools and workflows such as design, validation, analysis, documentation, quality and operations.
• Ensure compliance with AI governance, cybersecurity, intellectual property and Responsible AI principles, and validate AI outputs before engineering use.
• Promote adoption by educating teams, supporting pilots and proofs of concept, and encouraging reuse of successful solutions.
• Share patterns, reusable components and lessons learned with the wider Wind Engineering AI community and give input to enterprise architecture teams.
• Maintain deployed solutions, their documentation and support models, and track investments, performance and benefits.

Required profile

• Bachelor's degree in Engineering, Computer Science, Data Science, Applied Mathematics, Systems Engineering or a related technical discipline.
• Experience designing, developing, deploying or supporting AI, machine learning, advanced analytics, automation or digital engineering solutions.
• Experience working directly with engineering teams to solve technical or operational problems through digital technologies.
• Strong problem-solving and systems-thinking skills, and the ability to communicate with technical and non-technical stakeholders.
• Demonstrated ability to influence change and drive adoption in engineering organizations.
• Preferred: experience applying AI in engineering, manufacturing, industrial, energy or highly technical environments; familiarity with GE Vernova digital platforms, ARC Foundry, AWS, Azure, AMP or similar ecosystems; hands-on experience with agentic AI frameworks, copilots, workflow orchestration tools or engineering knowledge management systems; experience in design automation, simulation acceleration, predictive engineering, FMEA support or validation workflows; strong understanding of subsystem engineering processes; comfort in a matrixed organization.

Required skills

• Working knowledge of generative AI, RAG, AI agents, machine learning and workflow automation.
• Preferred: agentic AI frameworks, copilots, workflow orchestration tools, engineering knowledge management systems.