Senior AI Architect

GE Vernova

Greenville, United States

Full Time

Posted August 31, 2026

$113k - $113k

Power Generation

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Listing evidence and provenance

Source: workday View original listing
Freshness: Open in the current public read path; this is not an employer guarantee.Last observed October 1, 2026
Classification: Modern power-system classification not yet published for this listing.Legacy source industry field: Power Generation
Pay provenance: Employer-provided pay

Job Description

The Senior AI Architect is an enterprise technical authority responsible for defining scalable AI architectures and guiding consistent implementation across Wind Engineering.

Key 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.
Ensure reference architectures are aligned 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.

Requirements

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.
Experience defining technical standards, reference architectures, and design practices across a portfolio of AI solutions.
Familiarity with MLOps platforms, cloud AI infrastructure (AWS, Azure, or GCP), model serving frameworks, and enterprise data platforms.
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.
GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).