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Sr AI Solutions Architect Blades
Job Location:
Monthly Salary:
Posted:
19 October 2026 (17 hours ago)
Posted:
Application Deadline:
16 January 2027
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Job Description Summary
The AI Solutions Architect - Blades is a Blade subsystem-aligned leader responsible for identifying designing deploying and sustaining AI solutions that deliver measurable value within a specific Wind Blades 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 to Blades integration & management 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 Blade Subsystem Priorities
- Partner with blade integration & management leader 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
- Bachelors 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
Required Experience:
Senior IC
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