**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.

Job Description

Key Responsibilities

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.

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.

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.

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.

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.

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.

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, or equivalent experience.
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