Director, Test Architecture

Ford•Dearborn, MI

About The Position

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.

Requirements

Proven experience at a Director or senior leadership level within software, software architecture, test management, systems integration, software quality, or automotive product development.
Demonstrated ability to lead, mentor, and inspire global teams of managers and technical leaders, establish operating models, and drive execution across complex vehicle programs and software release plans.
Experience with enterprise lifecycle and test management tools such as Jira, Test Case Management, Program Management, dashboards, KPI reporting, and end-to-end traceability methodologies.
Exceptional communication and presentation skills, with a track record of articulating complex technical risks, bottlenecks, status, and recommendations clearly to senior executives and external partners.
Deep technical proficiency in modern software development methodologies (Agile, SAFe) and toolchain environments.

Nice To Haves

Strong, deep knowledge of test planning, test execution governance, defect management, risk mitigation, software release readiness, milestone reviews, and quality escalation processes.
Experience integrating test automation, AI-enabled testing methodologies, process optimization, and advanced data analytics into test management practices.
Experience integrating test automation, AI-Assisted testing methodologies, AI-Led optimization patterns, process optimization, and advanced data analytics into test management practices.

Responsibilities

Define and own the reference architecture for AI-Assisted and AI-Led V&V test frameworks, including reusable patterns, common libraries, data interfaces, orchestration models, reporting integration, and governance standards.
Shape how Ford models testing operations around AI, moving beyond tactical automation fixes to a predictive, closed-loop, intelligence-driven V&V capability.
Lead technical value stream mapping across requirements, test design, test planning, execution, defect triage, analytics, and release readiness to identify where AI-Assisted workflows can augment engineering decisions and AI-Led capabilities can automate repeatable, data-driven interventions to reduce waste, improve flow, and increase engineering leverage.
Architect scalable frameworks for test automation, AI-assisted Gherkin authoring, automated test code generation, smart regression selection, failure pattern detection, coverage heat maps, and reusable test assets.
Develop multi-year technical roadmaps for AI-Assisted and AI-Led V&V innovation, including pilots, reference implementations, adoption milestones, KPI targets, and convergence plans across teams and toolchains.
Lead through influence across V&V, software engineering, systems engineering, DevSecOps, simulation, lab infrastructure, analytics, and product teams to drive adoption of common technical standards without relying on direct reporting authority.
Define how test data, defect data, requirements traceability, execution evidence, and release metrics should be structured and connected to enable AI-Assisted insights, AI-Led closed-loop recommendations, and measurable improvements in quality outcomes.
Establish technical standards for framework design, repository structure, data quality, model usage, evidence capture, traceability, configuration management, security, and responsible AI practices within V&V.
Identify, prototype, and scale high-impact AI-Assisted and AI-Led use cases such as autonomous test exploration, requirements-to-test generation, anomaly detection, root-cause assistance, test optimization, and predictive release readiness.
Translate complex AI, data, and test architecture concepts into clear technical recommendations, business cases, implementation plans, and leadership-ready narratives tied to quality, speed, cost, and risk reduction.