Director, Test Architecture
- Compensation
- Not disclosed
- Location
- Dearborn, MI, United States
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Not specified
- Posted
Skills
Must have
- Senior Leadership Experience: Proven experience at a Director or senior leadership level within software, software architecture, test management, systems integration, software quality, or automotive p
- Global Team Leadership: Demonstrated ability to lead, mentor, and inspire global teams of managers and technical leaders, establish operating models, and drive execution across complex vehicle program
- Modern Tool Proficiency: Experience with enterprise lifecycle and test management tools such as Jira, Test Case Management, Program Management, dashboards, KPI reporting, and end-to-end traceability m
- Executive Communication: Exceptional communication and presentation skills, with a track record of articulating complex technical risks, bottlenecks, status, and recommendations clearly to senior exec
Nice to have
- V&V Expertise: Strong, deep knowledge of test planning, test execution governance, defect management, risk mitigation, software release readiness, milestone reviews, and quality escalation processes.
- Advanced Testing Innovation: Experience integrating test automation, AI-enabled testing methodologies, process optimization, and advanced data analytics into test management practices.
- Advanced Testing Innovation: Experience integrating test automation, AI-Assisted testing methodologies, AI-Led optimization patterns, process optimization, and advanced data analytics into test manage
Full 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.
Key Responsibilities
- AI Test Architecture Leadership: 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.
- Future-State V&V Operating Model: Shape how Ford models testing operations around AI, moving beyond tactical automation fixes to a predictive, closed-loop, intelligence-driven V&V capability.
- AI-Assisted Value Stream Mapping and AI-Led Optimization: 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.
- Test Framework Modernization: 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.