Director, Test Architecture

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.

What You''ll Bring (Qualifications)

Must-Have Qualifications:

• Senior Leadership Experience: Proven experience at a Director or senior leadership level within software, software architecture, test management, systems integration, software quality, or automotive product development.
• 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 programs and software release plans.
• 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 methodologies.
• Executive Communication: 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.

Nice-to-Have Qualifications:

• 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 management practices.

Role Evaluation Criteria

• Communication: Requires executive-level communication skills to present highly technical data, execution bottlenecks, risk assessments, and readiness status as clear, actionable strategic updates to senior leadership, chief reviewers, and cross-functional forums.
• Discretion / Latitude: Exercises high latitude and decision-making authority over the global software test management strategy, setting validation baselines, gating software releases, and determining the deployment of department budgets and supplier resources.
• Influence: Exerts broad influence across VV organizations, software development teams, program steering committees, and quality forums to ensure digital integrity standards and robust validation protocols are prioritized.
• Impact: Directly impacts the quality, reliability, and security of Ford''s on-board and off-board digital products, preventing software escapes and ensuring seamless vehicle launches.
• Problem Solving: Solves highly complex, multi-variable engineering challenges related to defect tracking, resource constraints, system bottlenecks, and software integration issues across global programs.
• Scope: Global oversight covering test planning and management across all designated vehicle platforms, on-board/off-board software products, and global program execution.
• Strategy: Drives the long-term roadmap for V&V tool modernization, and engineering toolchain integration (e.g., Jira, Test Case Management, automated KPI reporting).
• Technical Expertise: Combines deep technical proficiency in modern software development methodologies (Agile, SAFe) and toolchain environments.

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.

• Technical Strategy & Roadmap: 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.

• Cross-Functional Technical Influence: 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.

• Data, Analytics & Closed-Loop Intelligence: 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.

• Governance, Standards & Reuse: 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.

• Innovation Incubation: 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.

• Executive Technical Communication: 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.

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.

• Technical Strategy & Roadmap: 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.

• Cross-Functional Technical Influence: 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.

• Data, Analytics & Closed-Loop Intelligence: 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.

• Governance, Standards & Reuse: 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.

• Innovation Incubation: 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.

• Executive Technical Communication: 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.