AI Workflow Project Manager Electrical Engineering Automation

GE Vernova•Markham, ON

4d•$98,400 - $164,000•Remote

About The Position

GE Vernova is seeking an experienced AI Workflow Project Manager Electrical Engineering Automation to lead the transformation of protection and control engineering from manual execution to an AI-assisted, human-verified delivery model. In this role, you will lead the development and deployment of an AI-enabled workflow that supports the full lifecycle of P&C Projects. Working across engineering, droughting, testing, manufacturing, and digital teams to build a scalable toolchain that improves speed, quality, repeatability, and traceability while keeping human engineering judgment at the center of the process. This role is ideal for a technically credible project leader who combines firsthand electrical engineering knowledge with experience delivering AI or automation solutions in an industrial environment.

Requirements

Bachelor’s degree in electrical engineering, Power Systems Engineering, Computer Engineering, or a related field with strong electrical specialization.
Minimum of 5 years of experience in protection and control engineering, power systems engineering, or related electrical project delivery.
Experience delivering AI-enabled tools, engineering automation systems, or digital workflow solutions in a technical or industrial setting.
Excellent communication and stakeholder engagement skills, with the ability to communicate effectively across technical and non-technical audiences.

Nice To Haves

Demonstrated experience in P&C panel design, manufacturing, FAT/SAT, commissioning, or equivalent technical project environments.
Knowledge of relevant standards such as IEC 61439, IEC 60255, IEC 62271, IEC 61850, NEMA, CSA, or equivalent.
Proven experience managing cross-functional technical projects with responsibility for scope, schedule, budget, and stakeholder alignment.
Ability to work effectively with software developers, data teams, and engineers as both a domain expert and product owner.
Demonstrated change leadership and the ability to drive adoption of new tools and workflows across experienced engineering teams.

Responsibilities

Lead the design, implementation, and operationalization of an end-to-end AI workflow for electrical engineering automation across the project lifecycle.
Define and integrate AI-enabled tools and platforms, including large language models, structured data pipelines, generative engineering workflows, and automated drawing solutions.
Build and maintain the workflow architecture for requirements capture, feasibility analysis, solution design, wiring generation, FAT script generation, SAT script generation, and continuous improvement.
Define the data structures, prompt frameworks, engineering knowledge libraries, and traceability methods required to support high-quality AI outputs.
Establish human verification checkpoints at each workflow stage, including approval criteria, reviewer responsibilities, and structured feedback capture.
Deploy AI-assisted tools that interpret customer RFQs, specifications, single-line diagrams, and technical data to generate structured engineering requirements and feasibility outputs.
Drive AI-assisted solution development, including single-line diagrams, protection schemes, relay recommendations, bill of materials, configuration outputs, and panel layouts.
Lead the implementation of automated wiring drawing generation using industry tools such as AutoCAD Electrical, or equivalent platforms.
Build AI-driven FAT and SAT procedure generation capabilities using approved design packages, drawings, relay settings, and site-specific constraints.
Drive adoption across teams by delivering training, change management support, and structured methods for interacting with and validating AI-generated outputs.
Define and track key performance indicators such as first-pass accuracy, verification cycle time, drawing revision rates, and test script quality.
Lead continuous improvement efforts by capturing feedback from project execution, as-built data, test outcomes, and user reviews to improve future AI performance.