Responsible AI Lead

Role Mission

You establish and run the company's Responsible AI framework so that every AI, agentic, and Physical AI system the company builds or buys is safe, secure, compliant, transparent, and trusted — by the workforce on the plant floor, by customers, and by regulators.

You make responsible practice a source of speed and confidence, not a brake: clear guardrails that let Pods move quickly because the rules of the road are known in advance. You own the risk, governance, and trust dimension of the enterprise AI program — and you carry the authority to halt a deployment that is not safe or sound.

Key Responsibilities

Responsible AI Framework & Policy

• Author and own the company's Responsible AI principles, policy, and standards — tailored to a manufacturing enterprise where AI increasingly touches physical operations and worker safety.
• Translate principles into practical, testable controls that fit into the delivery pathway, so "responsible" is something engineers can implement rather than an abstraction.
• Maintain the enterprise AI use-case and model inventory, with risk tiering that scales oversight to potential impact.

AI Governance & Model Risk Management

• Stand up and chair the AI Governance / Ethics Review function; define the review cadence, risk-tiering criteria, approval thresholds, and escalation paths to the Steering Committee.
• Establish model and agent risk management: documentation (model and agent cards), independent validation and challenge, bias and fairness testing, performance and drift monitoring, and decommissioning.
• Define accountability and human-oversight requirements for agentic workflows — where autonomy is permitted, what requires a human in or on the loop, and how actions are logged and reversible.

Safety & Physical AI Assurance

• Partner with plant safety, engineering, and the Steel Domain SME to ensure Physical AI systems (vision, robotics, autonomous handling, closed-loop control) meet functional-safety and human-safety expectations before and during deployment.
• Define the assurance approach for AI that influences process control or operates near people — hazard analysis, safe-state and fallback behavior, validation, and continuous monitoring.
• Ensure agentic and autonomous systems fail safe and remain within authorized operating envelopes.

Security, Privacy & IP Protection

• Partner with Security and the Architecture Lead to address AI-specific threats — prompt injection, data poisoning, model and output exfiltration, adversarial attacks on vision systems — and to protect the company's proprietary process data and models as core intellectual property.
• Set data-privacy and data-governance requirements for AI, covering employee, customer, and third-party data and the appropriate use of external / foundation models.

Regulatory, Standards & Assurance

• Maintain the enterprise's line of sight to the evolving AI regulatory and standards landscape (e.g., NIST AI RMF, ISO/IEC 42001, the EU AI Act where extraterritorial, emerging US state and sector rules) and translate obligations into the company's requirements.
• Own third-party / vendor AI risk assessment and the contractual guardrails for AI partners and tools.
• Prepare the enterprise for internal and external audit and assurance of its AI program.

Workforce Trust & Responsible Change

• Partner with HR, Operations, and communications to build workforce trust — transparency about how AI is used, how it affects roles, and how its decisions can be questioned.
• Advise on responsible workforce transition as automation and agentic workflows change how work is done, consistent with the company's culture and values.
• Serve as an approachable, credible point of contact for concerns raised about AI systems.

Required Qualifications & Experience

• 15+ years in AI / ML governance, risk, safety, compliance, cybersecurity, or a closely related discipline, with senior accountability for enterprise risk or governance programs.
• Demonstrated experience building and running a governance, risk, or assurance framework in a regulated, safety-sensitive, or industrial environment.
• Strong working knowledge of AI / ML and generative / agentic systems — enough to challenge technical teams credibly on bias, robustness, security, and oversight.
• Command of the AI risk and standards landscape (NIST AI RMF, ISO/IEC 42001, model-risk-management practice, relevant regulation).
• Track record influencing executives and operating leaders and embedding controls without blocking delivery.
• Understanding of manufacturing operations and industrial / functional safety sufficient to govern Physical AI responsibly.

Preferred Qualifications

• Experience in steel, metals, heavy industry, or another safety-critical operating environment.
• Familiarity with functional safety (e.g., IEC 61508 / machinery-safety concepts) applied to autonomous or AI-influenced systems.
• A background spanning both AI / data and risk / compliance / security.
• Relevant certifications — welcome but secondary to demonstrated judgment and results.