EmergingAI assurance, audit and standards

AI Management Systems Specialist

Also called AI Management System Lead

An AI Management Systems Specialist builds and runs the processes that keep an AI company's governance real: policies, controls, evidence and corrective actions. It matters because customers and regulators increasingly ask for proof, not promises.

What they do

They design the management system around how AI is built and used, then make it work. They map the AI lifecycle, set out roles and controls, and keep a control matrix current. They organise evidence in a repository so it can be found when asked for. They run internal reviews against an audit programme, record findings and chase corrective actions until they close. They sit with engineers and product teams to fit controls into existing workflows. They also brief leadership on gaps, and prepare the company for external audits and customer assurance requests.

What a strong hire delivers

In the first months, a strong hire gives the company a clear map of its AI systems and where governance applies. They put in place a working set of documented processes, owners and review records. They run a first internal review, log findings and see the first corrective actions through to closure. Leadership gets a plain view of coverage and open risks, and teams know what is expected of them.

How you can tell they are good

Ask them to walk through a management system they built or ran, and show sample records with sensitive detail removed. Good candidates explain why they chose each control, how they got engineers to follow it, and what they changed when it failed. Worry if they talk only about policy documents, or cannot describe a corrective action that actually worked.

Why it is hard to fill

The title barely exists yet. The work is often done by someone called quality manager, risk lead, compliance analyst or auditor, with AI added on the side. Few people combine real management-system experience with enough technical grasp to talk to ML teams. Candidates tend to be strong on one side only, so you have to choose which gap you can coach.

Where to find them

Look first at people from audit, assurance, quality management and technical risk, especially those who have implemented a management system in a regulated or safety-minded setting. Others come from information security, privacy, or governance teams at AI-heavy software companies, consultancies and certification bodies. Some are engineers or evaluation specialists who drifted into governance. Their job titles will often not mention AI.

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 Management Systems Specialist

  1. Tell us about a management system you implemented. What did you build, and what was working when you left?

    Strong answers name scope, owners, controls and evidence, and give a concrete sign it was used, such as closed actions or reviews held on time. Worry if they describe only documents written, or take credit for team work without detail.

  2. How would you decide which of our AI systems fall inside the management system first?

    Look for a risk-based approach: inventory, use context, impact, and data sensitivity. They should ask questions before answering. Worry about a one-size-fits-all answer or an attempt to cover everything at once.

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

Job description you can post

We build and deploy AI systems, and we want our governance to stand up to scrutiny from customers and regulators. We are hiring an AI Management Systems Specialist to design, implement and maintain the processes that make that possible, working closely with engineering and product teams.

What you will do

  • Design and maintain our AI management system, including policies, roles and controls
  • Keep a control matrix that reflects how we build and use AI
  • Organise and maintain evidence in a central repository
  • Plan and run internal reviews under an audit programme
  • Record findings and drive corrective actions to effective closure
  • Support external audits and customer assurance requests
  • Report process coverage and open risks to leadership

What you will bring

  • Demonstrated work implementing or running a management system
  • Experience in audit, assurance, quality management or technical risk
  • Working knowledge of AI risk management and governance practice
  • Enough understanding of model evaluation to review evidence critically
  • Clear technical communication with engineers and executives
  • Examples of corrective actions you have seen through to real improvement

Nice to have

  • Experience with certification or external audit preparation
  • Background in software or machine learning engineering
  • Experience in a regulated or safety-related sector

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

Skills that matter

  • AI governance and assuranceAdvancedAssign accountability and verify lifecycle controls
  • AI risk managementAdvancedIdentify, assess, treat and monitor AI risks
  • Model evaluationWorkingMeasure capability and failure modes credibly
  • 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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