Hiring nowResponsible AI governance and risk

Responsible AI Lead

Also called Responsible AI Manager

A Responsible AI Lead sets who owns AI risk and makes sure the controls around it work. They matter because customers, regulators and boards now ask how AI is governed, and someone has to answer with evidence.

What they do

They build and run the way a company governs its AI. They keep a model inventory, so everyone knows what is deployed and who owns it. They write policies that teams can actually follow, and design the checks that sit in the delivery process. They run risk and impact assessments with product and engineering, track open control gaps in a risk register, and chase owners until gaps close. They collect evidence for audits and customer reviews, and they report to leadership in plain terms.

What a strong hire delivers

In the first months, a strong hire delivers a complete inventory of AI systems with named owners. They agree a governance operating model that says who decides what. They put a monitored control plan in place, with evidence captured as part of normal work. They close the most material control gaps first, and leadership gets a clear, regular view of risk.

How you can tell they are good

Ask them to walk through a governance process they built and what changed because of it. Look for specifics: owners named, gaps closed, trade-offs made. Ask how they handled a team that resisted a control. Strong people explain technical issues clearly to engineers and lawyers alike. Be wary of answers that list frameworks but show no outcomes.

Why it is hard to fill

Titles vary widely, and the work sits between risk, compliance, legal and engineering. Few people have run AI governance end to end, so most good candidates come from adjacent fields and have learned the AI side recently. Many hold policy roles with no delivery authority. We screen for people who have made controls work in practice, not just written them.

Where to find them

They come from risk management, compliance, internal audit and technology governance, plus privacy and security assurance teams. Some are already in responsible AI or trust and safety functions. Look in financial services, healthcare, consultancies and larger technology firms, where governance is established. Model risk specialists often move across well.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Responsible AI Lead

  1. Tell me about an AI or technology governance process you built from nothing. What did it cover and who owned it?

    A strong answer names scope, owners and decision rights, and explains what changed afterwards. Worry if they describe only documents or frameworks, or take credit for work that a wider team delivered.

  2. How would you build a model inventory when teams are already shipping and nobody has tracked what is live?

    Good answers start with discovery through engineering, procurement and product, set a minimal record, and assign owners. They prioritise by risk. Worry if they propose a big survey and wait for responses.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring a Responsible AI Lead to set how we govern AI across the company. You will make ownership clear, run the controls that manage risk, and give leadership and customers confidence that our AI is used responsibly.

What you will do

  • Design and run our AI governance operating model
  • Maintain a model inventory with named owners
  • Write practical AI policies and embed controls in delivery
  • Run risk, fairness and impact assessments with product and engineering
  • Track control gaps in a risk register and drive them to closure
  • Prepare evidence for audits and customer reviews
  • Report AI risk clearly to senior leadership

What you will bring

  • Demonstrated governance or risk work on technology or AI systems
  • Experience turning policy into controls that teams follow
  • Working knowledge of AI risk and assurance practices
  • Able to assess fairness and impact in a real product context
  • Clear written and spoken communication with technical and non-technical people
  • Track record of closing control gaps through influence

Nice to have

  • Experience in a regulated sector
  • Background in audit or model risk
  • Familiarity with policy and evidence management systems

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • AI risk managementAdvancedIdentify, assess, treat and monitor AI risks
  • AI governance and assuranceAdvancedAssign accountability and verify lifecycle controls
  • Fairness and impact assessmentWorkingAssess unequal errors, access and downstream impacts
  • Technical communicationWorkingExplain evidence and limitations to relevant readers

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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