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Director, Test Architecture
Career Insights for Generative Artificial Intelligence Engineer
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What they do
A Generative Artificial Intelligence Engineer develops, designs, and manages generative models and algorithms that support the generation of new content in the form of images, text, audio, and other multimedia. They utilize GPTs, GANs, VAEs, and other deep learning architectures to craft systems capable of generating data. May work with data scientists, machine learning engineers, and software developers.
$123,000 / year median in Michigan
Job Description
The Role The Director, Test Architecture is a senior individual contributor leadership role responsible for defining the future technical direction of Verification & Validation test architecture. This role will serve as the principal technical authority for AI-Assisted and AI-Led testing innovation, test framework architecture, and next-generation V&V operating models across on-board and off-board software products. Working through technical influence rather than direct people management, this person will architect scalable test frameworks, reusable automation assets, AI-assisted test design patterns, intelligent regression strategies, and closed-loop analytics that transform V&V from a reactive execution function into a predictive, automation-first, data-driven, software first engineering capability. The ideal candidate is self-driven, deeply technical, and future-facing—able to look beyond immediate process gaps and define how Ford should model, optimize, and scale testing operations around AI. This includes leading AI-Assisted value stream mapping for human-in-the-loop analysis and AI-Led operating-model design for autonomous optimization, identifying high-value intervention points across the V&V lifecycle, and translating emerging AI/ML capabilities into practical architecture, standards, governance, and measurable quality outcomes. This role will partner across V&V, software engineering, systems engineering, DevSecOps, data analytics, simulation, lab infrastructure, and product teams to establish a common AI-Led test architecture that improves coverage, cycle time, defect detection effectiveness, traceability, reuse, and release confidence.