Director, Responsible AI & Emerging Technology

AAM•Detroit, MI

About The Position

The Director of AI & Emerging Technology is accountable for defining, governing, and delivering enterprise AI capabilities that are scalable, secure, ethical, and value driven. This leader will report to the Chief Information Officer (CIO) and own the AI and emerging technology roadmap, translating business priorities into practical AI solutions across manufacturing, supply chain, quality, engineering, and corporate functions. The role blends strategic leadership, technical depth, and governance accountability—ensuring Responsible AI principles and AI security are embedded by design, while also driving real operational outcomes through production-grade AI and MLOps/LLMOps platforms. This leader serves as a bridge between business value creation, advanced AI engineering, and risk management, guiding the organization from experimentation to industrialized AI at scale.

Requirements

Master’s degree in relevant discipline.
15+ years progressive information technology experience with strong technical background and deep cybersecurity, risk, and secure-by-design expertise.
10+ years management and strategy definition/execution experience.
Experience as a mentor and subject-matter leader in Responsible AI and emerging technologies

Responsibilities

AI Strategy, Roadmap & Value Delivery Define and own the enterprise AI and emerging technology roadmap, aligned to business strategy and manufacturing priorities (e.g., productivity, quality, cost, safety, resilience).
Identify, prioritize, and sequence AI use cases based on value, feasibility, risk, and scalability, moving the organization from pilots to sustained production outcomes.
Partner with Operations, Engineering, Supply Chain, Quality and Corporate functions to co-create AI solutions that deliver measurable business impact.
Establish patterns and reference architectures for repeatable, scalable AI solutions across plants, regions, and business units.
Oversee delivery of AI initiatives through clear milestones, success metrics, and lifecycle ownership—from ideation through decommissioning.
Responsible AI Governance & Risk Management Architect and operationalize Responsible AI governance frameworks covering the full AI lifecycle, aligned with regulations and standards (e.g., EU AI Act, NIST AI RMF, ISO/IEC 42001).
Design tiered AI risk classification models to guide approval paths, controls, and monitoring requirements based on use-case criticality.
Chair or co-lead cross-functional AI governance councils, partnering with Risk, Security, Data, and Business leaders to evaluate higher-risk AI deployments.
Ensure transparency, traceability, and accountability in AI systems, including documentation, model lineage, and decision explainability appropriate to risk level.
Serve as a trusted advisor to executives and boards on AI risk posture, regulatory readiness, and maturity progression.
AI Security & Trustworthy AI Engineering Build and lead AI-focused security practices addressing adversarial ML, model integrity, data provenance, and secure deployment.
Conduct AI threat modeling and risk assessments across models, data pipelines, vendors, and deployment environments.
Develop and execute AI red-teaming and adversarial testing programs (evasion, poisoning, model extraction, prompt injection, misuse scenarios).
Define and enforce secure-by-design AI patterns, embedding governance and security controls into MLOps/LLMOps pipelines and CI/CD workflows.
Partner with Cybersecurity and Incident Response teams to integrate AI-specific risks, signals, and playbooks into enterprise security operations.
Leadership, Enablement & Change Build and mentor multidisciplinary teams spanning AI engineering, governance, and security.
Drive organizational AI maturity through training, enablement, and change management for both technical and non-technical stakeholders.
Establish clear communication and reporting to demonstrate AI value, risk management, and compliance to leadership, auditors, and external stakeholders.
Act as a visible thought leader on AI, emerging technology, and Responsible AI practices within the enterprise.
AI Platforms, MLOps & Industrialization Guide the design and evolution of enterprise AI platforms
Establish standards for MLOps/LLMOps, enabling reliable deployment, monitoring, rollback, and continuous improvement of AI systems.
Implement monitoring for model performance, drift, bias/fairness, robustness, and misuse, coordinating remediation where thresholds are exceeded.
Ensure AI solutions are production-ready, scalable, and supportable within a global manufacturing environment.