Senior AI Solution Architect

Job Description Summary

The AI Solutions Architect is a subsystem-aligned leader responsible for identifying, designing, deploying, and sustaining AI solutions that deliver measurable value within a specific Wind Engineering domain. AI Solutions Architects serve as the bridge between AI capabilities and engineering competencies, translating business and technical challenges into practical AI-enabled solutions that improve productivity, quality, speed, and engineering outcomes.

Reporting within a Subsystem team, with a dotted-line technical relationship to a Senior AI Architect, the AI Solutions Architect works closely with engineers, technical leaders, product owners, and process leaders to integrate AI into day-to-day engineering workflows. Impacts approaches, projects and programs in the functional area or affected business organization and ways of working. Impacts quality, efficiency and effectiveness of own team. Has significant input into priorities. Guided by professional practices and policies that are shaped by the role. The role has moderate autonomy, requiring high levels of operational judgment.

Job Description

1. Identify and Deliver AI Use Cases Aligned to Subsystem Priorities

• Partner with subsystem leadership to identify high-value opportunities where AI can improve engineering effectiveness, quality, cycle time, cost, or risk outcomes.
• Translate business and engineering pain points into prioritized AI use cases with defined objectives and measurable success criteria.
• Build and maintain a subsystem AI opportunity roadmap aligned with business priorities and Wind Engineering transformation goals.
• Evaluate potential use cases for technical feasibility, data availability, expected business value, and implementation complexity.
• Serve as the primary AI advisor and thought partner for subsystem leadership.

2. Design and Deploy AI Solutions Using Approved Architectures and Patterns

• Design AI-enabled solutions that align with approved platforms and established design standards.
• Configure, develop, and deploy AI applications, agentic workflows, copilots, automation solutions, predictive models, and knowledge systems within the subsystem domain.
• Participate in architecture reviews and design discussions led by Senior AI Architects.
• Ensure deployed solutions are maintainable, scalable, and supportable within the broader Wind Engineering AI ecosystem.

3. Integrate AI into Engineering Tools and Workflows

• Embed AI solutions into existing engineering processes, applications, and workflows to drive practical adoption and measurable impact.
• Work with engineering teams to integrate AI capabilities into design, validation, analysis, documentation, quality, and operational activities.
• Support the digitization and automation of engineering knowledge and decision-making processes.

4. Ensure Compliance with AI Governance, Security, and Quality Standards

• Design and deploy solutions in accordance with Wind Engineering AI governance requirements, cybersecurity standards, intellectual property protections, and Responsible AI principles.
• Ensure AI outputs are appropriately validated before use in engineering decisions or records.
• Escalate architectural, technical, or governance concerns through defined review processes.

5. Drive Adoption and Change Within the Subsystem

• Act as the local champion for AI adoption and capability development within the subsystem.
• Educate engineers and technical teams on emerging AI capabilities and practical applications.
• Support pilots, proofs of concept, and scaled deployments by helping teams understand and trust AI-enabled workflows.
• Encourage reuse of successful AI solutions and lessons learned across the subsystem.

6. Collaborate with Senior AI Architects and the Broader AI Community

• Contribute new patterns, lessons learned, reusable components, and technical innovations back to the Wind Engineering AI community.
• Provide input to enterprise architecture teams on emerging subsystem requirements and opportunities.
• Share successful implementations that may be expanded or scaled across multiple engineering domains.

7. Sustain and Improve Deployed AI Solutions

• Monitor deployed AI solutions for performance, quality, reliability, business value, and adoption.
• Identify opportunities for enhancement, optimization, reuse, or retirement of existing solutions.
• Ensure appropriate documentation, support models, and ownership structures are established for sustained operations.
• Maintain visibility into subsystem AI investments, performance, and realized benefits.

Required Qualifications

• 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.
• Working knowledge of modern AI technologies, including generative AI, retrieval-augmented generation (RAG), AI agents, machine learning, and workflow automation.
• Experience working directly with engineering teams to solve technical or operational problems through digital technologies.
• Strong problem-solving and systems-thinking skills.
• Ability to communicate effectively with both technical and non-technical stakeholders.
• Demonstrated ability to influence change and drive adoption within engineering organizations.

Preferred Candidate Characteristics

• Experience applying AI within engineering, manufacturing, industrial, energy, or highly technical environments.
• Familiarity with GE Vernova digital platforms, ARC Foundry, AWS, Azure, AMP, or related enterprise technology ecosystems.
• Hands-on experience with agentic AI frameworks, copilots, workflow orchestration tools, or engineering knowledge management systems.
• Experience implementing AI-enabled solutions for design automation, simulation acceleration, predictive engineering, FMEA support, validation workflows, or engineering productivity improvement.
• Strong understanding of subsystem engineering processes and engineering lifecycle activities.
• Ability to balance innovation with engineering rigor, quality, and governance requirements.
• Comfortable operating in a matrixed organization with both local and enterprise stakeholders.
• Continuous learning mindset with strong curiosity about emerging AI technologies and engineering applications.

Additional Information