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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:

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

Relocation Assistance Provided: No

📌 Sr AI Solutions Architect (Barcelona)
🏢 GE Vernova
📍 Barcelona