Blades Digital Engineering & AI Specialist

About the Global Blade Innovation Center

Envision Energy’s Global Blade Innovation Center (GBIC) was established in 2015 to build a world‑class, in‑house blade design capability. Engineers from industry‑leading OEMs, national laboratories, and top graduate programs have collaborated to create a state‑of‑the‑art design capability from the ground up. Envision’s in‑house blade designs and technologies have disrupted global markets and delivered significant reductions in Levelized Cost of Energy (LCOE) alongside measurable expansion of Envision’s market share.

The wind industry is at an inflection point in how engineering work gets done. GBIC is investing in the AI and digital engineering capabilities needed to stay at the leading edge, and this role is the architect of that effort.

As the Digital Engineering & AI Specialist, you will define, own, and execute the high‑level architecture of AI systems and tools that transform how the Blade Design team operates. This is not an implementation support role. You will determine what gets built, how it is structured, and how it connects to real engineering workflows. You will design and deploy AI agents, automation pipelines, and intelligent decision‑support systems that make the team faster, more consistent, and capable of solving problems at a scale and speed not otherwise possible.

This role requires equal command of the engineering domain and the AI/software toolkit. You need enough structural and wind engineering intuition to identify where AI can have genuine impact, and the technical depth to architect and build systems that engineers trust and use.

• Define the high‑level architecture of AI tools and systems for the Blade Design and Engineering teams, including agent frameworks, orchestration layers, data pipelines, and model integration patterns.
• Own the end‑to‑end AI development lifecycle: problem framing, system design, model selection and development, validation, deployment, and iteration.
• Design multi‑agent systems and agentic workflows that automate complex, multi‑step engineering tasks, from inspection data processing to RCA support to design evaluation.
• Establish standards, patterns, and reusable components for AI‑assisted engineering work products across the team.
• Develop, fine‑tune, and deploy AI/ML models for blade engineering applications including defect detection and classification; failure mode prediction; structural performance surrogate modeling; and manufacturing quality assessment.
• Apply physics‑informed and domain‑constrained modeling approaches where engineering knowledge can improve model reliability and generalizability.
• Validate AI/ML outputs rigorously against physical test data, field observations, and engineering expectations. Model confidence must be earned, not assumed.
• Build model monitoring and feedback loops that allow deployed systems to improve over time with new engineering data.

• Identify and automate high‑friction engineering workflows across blade design, reliability, and field operations, including analyses pipelines, inspection processing, reporting, and data aggregation.
• Build and maintain internal engineering tools, APIs, and platforms that directly integrate AI capabilities into day‑to‑day engineering practices.
• Collaborate with IT and data infrastructure teams to ensure engineering data is structured, accessible, and AI‑ready.
• Support structural health monitoring and in‑service data applications as one domain where AI tools add high value, including anomaly detection, damage identification, and condition‑based monitoring.

• Work closely with composite design and field reliability engineers to understand physical failure modes and translate domain knowledge into effective AI system architecture and model design.
• Communicate AI system capabilities, limitations, and outputs clearly to engineering stakeholders, earning trust through transparency, not just performance metrics.
• Champion responsible AI adoption within the team: clear validation standards, documented assumptions, and traceable outputs.
• Stay at the…