Senior AI Architect

Overview

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. Senior AI Architects mentor and develop Embedded AI Architects and provide technical oversight across the AI portfolio, ensuring that solutions built within subsystems conform to enterprise standards and can be reused across Wind Engineering.

This role is also responsible for updating engineering standard work and quality management systems to reflect AI-forward advancements. Reporting to the Director – AI Strategy & Transformation, the ideal candidate is a technically deep, architecturally minded leader who combines hands‑on AI engineering expertise with the ability to define and enforce standards, influence without authority, and operate at the intersection of AI, engineering, and process workflows.

• 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). Maintain a library of reusable AI solution patterns, components, and design templates for Embedded AI Architects. Establish architecture standards that address data ingestion, deployment, monitoring, versioning, and lifecycle management for AI solutions across Wind Engineering.
  
  Align reference architectures with GE Vernova enterprise platforms, ARC Foundry capabilities, Digital/IT infrastructure, and approved tooling. Regularly review and update architectures as AI technology, enterprise platforms, and Wind Engineering use cases evolve.

• 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. Establish standards for how AI solutions are scoped, designed, validated, deployed, and maintained across the portfolio. Develop design principles that prioritize scalability, reusability, and engineering workflow integration from the earliest stages of solution design. Create architecture review checkpoints within the portfolio stage‑gate process, ensuring technical quality is evaluated at key maturity transitions.

• 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. Lead the integration of AI‑assisted workflows into Wind Engineering standard processes, including AI‑assisted FMEA updates, design review workflows, and validation procedures. Ensure that AI tools and capabilities introduced into engineering workflows are supported by updated standard work documentation, training materials, and process guidance.
  
  Work with engineering leaders and quality teams to define how AI‑generated outputs are reviewed, validated, and incorporated into engineering records and decision‑making. Establish the technical baseline for how AI contributes to engineering quality.

• Partner with Digital and IT on platforms, MLOps, and integration standards: serve as the primary technical liaison between Wind Engineering AI programs and Digital/IT, ARC Foundry, and enterprise platform teams. Define MLOps and LLMOps requirements for Wind Engineering, including model training pipelines, deployment automation, performance monitoring, drift detection, and retraining governance. Evaluate platform options, integration approaches, and tooling choices that enable scalable, maintainable AI deployment across engineering environments.
  
  Ensure AI solutions built on enterprise platforms (AMP, AWS, Azure, GE Vernova digital infrastructure) leverage approved…