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. The Role 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. Key Responsibilities AI Architecture & System Design 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. AI/ML Model Development & Deployment 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. Engineering Automation & Workflow Integration 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. Domain Collaboration & Technical Leadership 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 leading edge of AI/ML for engineering applications; evaluate and introduce relevant advances in agentic AI, LLM tooling, and applied ML to the teamQualifications Required MS or PhD in Mechanical, Aerospace, Civil, or Structural Engineering, Computer Science, or a closely related field, with demonstrable wind or structural engineering domain knowledge. 5+ years of professional experience at the intersection of engineering and applied AI/ML or digital systems development and deployment. Demonstrated experience designing and deploying AI/ML systems for engineering or industrial applications, including system architecture decisions, not just model training. Experience building agentic AI systems, multi-agent frameworks, or LLM-integrated engineering workflows. Strong programming proficiency in Python; experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn. Working knowledge of composite blade or wind turbine structural behavior sufficient to evaluate whether model outputs are physically plausible. Proven ability to communicate complex technical concepts clearly across engineering, operations, and leadership audiences. Strongly Valued Experience with data pipeline development, signal processing, or time-series analysis in structural or condition monitoring contexts (e.g., SHM, NDT data, drone inspection imagery). Familiarity with LLM orchestration frameworks (LangChain, LlamaIndex, or similar) and prompt engineering applications. Background in physics-informed neural networks (PINNs) or other approaches that embed domain knowledge into model architecture. Experience with FEA/FEM tools (ANSYS, ABAQUS, or similar) and ability to use simulation data as AI/ML training input. Experience building internal engineering software platforms, REST APIs, or analytical dashboards used by engineering teams in production. Background in wind energy OEM, operator, or research environments. What We’re Looking For The ideal candidate thinks about systems. You don’t just build models; you design the architecture that makes a team of engineers more capable than they could be alone. You are energized by the gap between what AI can theoretically do and what gets trusted and used in engineering practice, and you know how to close it. You have the domain credibility to earn the confidence of experienced blade engineers, and the technical range to move from agentic framework design to model validation to deployment in a single week. Strong interpersonal, collaboration, and communication skills are essential. Envision’s culture is entrepreneurial and fast-moving; the ability to move between deep technical work and cross-functional collaboration is expected. Desire and ability to work effectively across cultural boundaries and international time zones is critical. Work Arrangement & Travel Work arrangement: Hybrid Travel: Up to 15% international travel, including field deployments and collaboration with global GBIC teams.