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Tier-1 employer and metro locations increase applicant density, but AI-engineering niche reduces competition.
Strong engineering domain context (wind) makes cross-industry transferability limited.
Specialized AI-engineering skillset required but no explicit years, causing moderate candidate filtering.
Lead identification, design, deployment, and sustainment of AI solutions within a Wind Engineering subsystem to improve engineering effectiveness and outcomes.
Translate subsystem business and technical challenges into prioritized AI use cases with measurable success criteria and maintain AI opportunity roadmap.
Integrate AI into engineering workflows, ensure compliance with governance standards, drive adoption, and collaborate with senior architects and AI community for solution improvement and scaling.
Bachelor's degree in Engineering, Computer Science, Data Science, Applied Mathematics, Systems Engineering, or related technical field, 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, RAG, AI agents, machine learning, and workflow automation.
Work Experience Required: Not explicitly mentioned in the JD.
Experience applying AI in engineering, manufacturing, industrial, energy, or similarly technical environments.
Familiarity with digital platforms such as GE Vernova, ARC Foundry, AWS, Azure, or related enterprise ecosystems.
Ability to operate in matrixed organizations balancing innovation with engineering rigor and governance, influencing change, and driving AI adoption within engineering teams.