Director, Test Architecture

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, Dev Sec Ops , 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.

• 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 tool chains.
• Cross‑Functional Technical Influence:
  Lead through influence across V&V, software engineering, systems engineering, Dev Sec Ops , 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…