Senior AI Architect
About the role
The Senior AI Architect is an enterprise technical authority responsible for defining scalable AI architectures and guiding consistent implementation across Wind Engineering. This role translates AI strategy into durable technical patterns, standards, and reference architectures that enable Wind Engineering to build AI solutions that are reusable, secure, maintainable, and aligned to enterprise platforms.
Responsibilities
Define and Maintain Enterprise AI Architectures and Reusable Solution Patterns
Design, publish, and maintain enterprise AI reference architectures that provide Wind Engineering with proven, scalable patterns for common AI solution types (e.g., agentic workflows, RAG-based knowledge systems, time-series forecasting, computer vision).
Develop and maintain a library of reusable AI solution patterns, components, and design templates that Embedded AI Architects can leverage to accelerate delivery and ensure consistency.
Establish architecture standards that address data ingestion, deployment, monitoring, versioning, and lifecycle management for AI solutions across Wind Engineering.
Create and Maintain AI Design Practices
Define Wind Engineering's AI solution design practices, including design review processes, architecture decision records, and technical documentation standards.
Develop design principles that prioritize scalability, reusability and engineering workflow integration from the earliest stages of solution design.
Update Engineering Standard Work and Quality Management Systems with AI Advancements
Identify opportunities to embed AI-forward practices into existing engineering standard work, design processes, and quality management system (QMS) documentation.
Partner with Digital and IT on Platforms, MLOps, and Integration Standards
Define MLOps and LLMOps requirements for Wind Engineering, including model training pipelines, deployment automation, performance monitoring, drift detection, and retraining governance.
Review and Guide Subsystem AI Designs for Scalability and Reuse
Conduct architecture reviews for AI solutions being developed within subsystems and functions, evaluating alignment to enterprise standards, scalability, security, maintainability, and reuse potential.
Mentor and Develop Embedded AI Architects
Provide consistent technical mentorship, coaching, and development support to the Embedded AI Architect community across Wind Engineering subsystems.
Support Embedded AI Architects in navigating complex technical decisions, vendor evaluations, and architecture tradeoffs within their subsystems.
Qualifications
Bachelor's degree in Engineering, Computer Science, Applied Mathematics, Data Science, or a related technical field; advanced degree strongly preferred.
Significant hands-on experience designing, building, and deploying AI or machine learning solutions in complex technical environments, with demonstrated progression to enterprise-level architecture responsibilities.
Deep technical knowledge across AI/ML domains relevant to industrial engineering, including supervised learning, deep learning, time-series analysis, generative AI, agentic workflows, and physics-informed modeling.
Familiarity with MLOps platforms, cloud AI infrastructure (AWS, Azure, or GCP), model serving frameworks, and enterprise data platforms.
Desired Characteristics
Ability to partner effectively with engineering leaders, Digital/IT teams, platform owners, and governance stakeholders.
Strong communication skills, with the ability to explain complex technical concepts to non-technical audiences and translate architecture standards into practical guidance.
Practical experience with AI in engineering contexts, including design automation, simulation acceleration, FMEA support, predictive maintenance, anomaly detection, or AI-assisted validation workflows.
Experience with ARC Foundry, AMP, or GE Vernova enterprise AI platforms and integration patterns.
Familiarity with agentic AI frameworks (e.g., n8n, LangGraph, CrewAI) and experience designing multi-agent workflow architectures for engineering applications.
Knowledge of QMS integration requirements and experience embedding AI outputs into quality management and engineering documentation systems.
Experience mentoring and developing AI engineering talent across distributed teams or matrixed organizations.
Comfortable with Lean and engineering standard work concepts, with the ability to apply AI architecture thinking to process improvement and waste elimination.
Strong ownership mindset, equally comfortable driving technical strategy at the enterprise level and reviewing detailed subsystem-level design decisions.
Technical escalations are resolved promptly, with documented architecture decisions and rationale available for future reference.