Hiring nowResponsible AI governance and risk

AI Risk Manager

Also called AI Risk Officer

An AI Risk Manager identifies, assesses and tracks the risks in a company's AI systems against agreed tolerances. They give leaders a clear view of what could go wrong and what is being done about it.

What they do

They keep a live picture of AI risk across the business. They work from the model inventory and risk register, running assessments on new and changing systems. They talk to product and engineering teams to understand how a system is built and used, then judge where it could cause harm, bias or breach policy. They propose treatments, record decisions and chase the evidence that controls are working. When a risk exceeds tolerance, they escalate it to the right owner and make sure someone decides. They also prepare reporting for leadership and for audit.

What a strong hire delivers

In the first months, a strong hire gives the company a complete, current inventory of AI systems with each one risk-rated. They put a repeatable assessment process in place that teams actually follow. Open risks have named owners and treatment decisions on record. Evidence is gathered on time rather than at the last minute. Leadership gets a short, honest report on where exposure sits.

How you can tell they are good

Ask them to walk through a real risk assessment from first question to treatment decision. A good candidate explains how they judged severity, who they escalated to and what happened next. They can describe a model's behaviour in plain terms without waving it away. Listen for how they handled pushback from a team that wanted to ship. Be wary of generic frameworks with no live example.

Why it is hard to fill

The title is new and the backgrounds are mixed. Many candidates have solid risk or compliance experience but have never assessed a model. Others know the technology but have never owned a register or escalated a hard decision. People who do both are scarce. Job titles vary, so searching by title alone misses good people. We usually screen on the work they have done, not the label.

Where to find them

Most come from enterprise risk, compliance, internal audit or technology governance, often in regulated sectors such as financial services, insurance or healthcare. Others move over from responsible AI, privacy or information security teams. Some sit in model risk functions. AI product companies, consultancies and large organisations rolling out AI at scale are the main places to look.

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 an AI Risk Manager

  1. Describe an AI or model risk you assessed. How did you rate it and what treatment was decided?

    A strong answer names the system, the harm considered, the rating logic and the decision made. They should own their part. Worry if the answer stays at framework level or credits only the team.

  2. How do you decide when a risk needs escalating, and to whom?

    Look for clear tolerances, named owners and judgement about severity and speed. They should describe a real escalation and its outcome. Worry if they avoid escalating or treat it as someone else's call.

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

Job description you can post

We are hiring an AI Risk Manager to help us understand and control the risks in the AI systems we build and use. You will work with product, engineering and leadership to assess risks, agree treatments and keep clear evidence that our controls work.

What you will do

  • Maintain the inventory of AI systems and the AI risk register
  • Run risk assessments on new and changing systems
  • Assess fairness and impact risks with engineering and product teams
  • Recommend treatments and record decisions with named owners
  • Track evidence that controls are in place and working
  • Escalate risks that exceed agreed tolerances
  • Report on risk coverage and open items to leadership

What you will bring

  • Experience running risk assessments in risk, compliance, audit or technology governance
  • Examples of identifying and escalating risks that led to a decision
  • Working understanding of how AI models are built, tested and deployed
  • Ability to explain technical issues clearly to non-technical leaders
  • Experience managing registers, policies and evidence systems
  • Comfort challenging teams constructively

Nice to have

  • Experience with fairness or impact assessments
  • Background in a regulated sector
  • Familiarity with AI governance standards and assurance practice

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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