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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Solutions Architect - **Company:** GE Vernova - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Systems Engineering, Microsoft Azure, Digital Architecture, Electronic Design Automation, Failure Mode Effects Analysis, Knowledge-Based Systems, Machine Learning, Software Tools, Microsoft Copilot, Subsystems, Web Platforms, Workflow Management Systems, Enterprise Software Applications, System Availability, Generative AI, Agentic-AI, Information Technology, Engineering Base - **Published:** October 19, 2026 - **Apply:** https://www.adzuna.es/details/5931876320 ## About the Role * Strong and Proven experience in Wind Engineering Operation Tools. * Bachelor's degree in Engineering, Computer Science, Data Science, Applied Mathematics, Systems Engineering, or a related technical discipline, or equivalent experience. * 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. ## Description 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., 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.