NOTE: To use this guide, copy the content using the “Copy page” button above, then edit part two for your organisation.

Hiring an AI Operations Lead

This is the role enterprises reach for once the AI programme stops being a pilot. Someone inside the organisation who finds the work worth automating, builds it, runs it safely, and makes sure people use it.

Get the hire right and the platform agreement starts producing things you can put in front of a board. Get it wrong and you have an expensive licence and a folder of pilots.

The market has not standardised the title or the job description, which makes the search harder than it should be. Search for “AI Operations Lead” today and you will find four different jobs advertised under one name:

• Platform administration. Licences, token consumption, cost dashboards, access reviews, vendor coordination. Real work, closer to service management than to delivery.
• Agent performance tuning. Watching how deployed agents behave in one function, usually customer support, and improving them.
• MLOps. Model pipelines, training infrastructure, monitoring. A different discipline that shares three letters.
• AI enablement. Training, literacy programmes, internal comms.

Each is a slice of the job described here, and none is the whole of it. Post a generic advert and you will get applicants for whichever slice they have done before, which is how a search stalls at the shortlist.

This guide has three parts: what the role is, a job description you can edit, and how to interview for it.

There is also an installable Claude skill that works through it with you, above. It runs in three modes, because the three jobs happen weeks apart: decide whether to hire yet, write the job description against your platform and your functions, and build the interview from a real process of your own. The guide below is what the skill works through.

Part 1: Understand the role

The five responsibilities

An AI Operations Lead does five things.

Find the work. Sit with teams, map what they do, and identify the processes where an agent changes the economics. This is the half of the job that looks like consulting. It requires someone who can hold a credible conversation with a finance director about the month-end close.

Build the thing. Design and ship the agent or workflow. Write and test the instructions, wire up the connectors, handle the edge cases, get it into the tools people already work in. This is the half that looks like engineering.

Run the estate. Know what is deployed, who owns it, what each agent can reach, what it costs to run, and what happens when it goes wrong. As the number of agents grows this shifts from a side task to a real operational discipline.

Make it stick. A deployed agent nobody uses is a cost, not a return. The role owns the handover: training the team, building the internal champions, watching usage, and going back in when adoption stalls.

Keep up. Track what the platform can do that it could not last quarter, test it against the backlog, and retire the workarounds it makes redundant. Without someone whose job includes watching this, your agents stay built against last year’s constraints and nobody notices.

Is this a Forward Deployed Engineer?

Almost. The difference changes how you advertise the role.

Forward Deployed Engineer is a vendor-side role. Palantir created it in 2005 and Anthropic, OpenAI, Google and Databricks have since adopted it. An FDE is an engineer the vendor embeds inside a customer’s organisation to learn the domain and ship production code against the customer’s real data. An FDE writes production code on site. A solutions architect designs and rarely deploys. A customer success manager owns the relationship and commits no code.

An AI Operations Lead is that same person employed by you rather than by a vendor, pointed at your own organisation. The work is the same. The weighting is different: more adoption, more governance, more stakeholder work, less pure engineering.

The phrase “internal forward deployed engineer” started appearing in 2026 as enterprises began hiring the profile directly instead of renting it. The reason applies to you: an internal hire keeps knowledge of your proprietary processes inside the organisation. If you are hiring for this now you are early, and you will not find a settled job description to copy from a peer.

If you advertise the role as Forward Deployed Engineer you will get stronger engineers in the pipeline and fewer people who are comfortable spending a Tuesday afternoon coaching the procurement team. You need both halves. Name the engineering content explicitly in the job description and use a title that does not scare off the hybrid profile.

How it differs from roles you already have

The AI Operations Lead fills the gap between “the business has a problem” and “there is a working agent in production that people use”. Nobody owns that gap today, which is why programmes stall in it.

Readiness test

Answer these before you open the requisition.

Hire now if:

1. There is a committed AI investment, a signed platform agreement or a board mandate with budget allocated.
2. A named executive above the role owns the AI outcome.
3. You have use cases identified, not an intention to find some.
4. Security has a position on what AI agents may reach, even a restrictive one.
5. Someone is already doing pieces of this job informally, and is at capacity.

Hire later if:

1. The role is the AI strategy. A delivery hire cannot invent the mandate, and asking them to puts it two levels below where it belongs.
2. Nobody senior has committed budget.
3. The job as scoped covers delivery, governance, training, support and platform administration across the whole organisation. That is three jobs.
4. IT security has no position and no appetite to form one. The role will stall at the first integration.

What the role needs from you

• A sponsor. Someone senior who will open the door to a function that does not want to be automated.
• A budget they control. Platform costs, tooling, and the freedom to spend without a business case per agent.
• System access, agreed up front. Decide with security before day one what agents may reach. Negotiate it per project instead and the first quarter becomes the first half.
• An operating model. A map of how the organisation works, which processes each unit owns, and where AI is landing today. Without it the first quarter is discovery that should already exist.

Part 2: The job description

Copy from here. Replace everything in square brackets.

AI Operations Lead

Location: [Location, or hybrid arrangement] Reports to: [Chief Information Officer] Contract: [Permanent, full time]

About the role

[Organisation] has committed to [AI platform] and is [describe where you are: running pilots in two functions / rolling out across the organisation]. We are hiring an AI Operations Lead to own delivery: to find the work worth automating, build it, run it safely, and make sure it gets used.

This is a hands-on role. You will spend your weeks in two places: with teams understanding how they work, and building the agents and workflows that change it. If you want to write an AI strategy, this is not the role. If you want to ship things people use every day, keep reading.

What you will do

Find the work

• Run discovery with teams across [functions in scope] to map processes and identify where AI changes the outcome
• Maintain a prioritised backlog of use cases, scored on impact, confidence and ease
• Size the opportunity honestly, including the ones not worth doing

Build and ship

• Design, build and test AI agents and workflows against real processes and real data
• Integrate with [your systems: M365, Salesforce, SAP, ServiceNow]
• Write, version and test agent instructions, and maintain them as processes change
• Ship into the tools people already use, [Microsoft Teams / Slack / Google Chat], rather than a separate destination

Run the estate

• Maintain the register of deployed agents: owner, purpose, systems reached, data handled, running cost
• Monitor usage, cost and failure modes, and act on them
• Work with [security and compliance] to keep deployments inside policy

Drive adoption

• Own handover: train the team, document the workflow, agree who supports it
• Build and support a network of internal champions across functions
• Track adoption and go back in when it drops
• Report progress to [the sponsor and steering group] on a [monthly] cycle

Keep current

• Track new platform capability and test it against the open backlog
• Retire the workarounds newer capability makes redundant
• Brief [the sponsor and steering group] on what has become possible since the last review

First 90 days

What we are looking for

Essential

• Evidence of building and shipping something with AI that other people use daily. We will ask you to walk us through it.
• Comfortable working directly with business teams, including ones sceptical about AI
• Able to map a business process and identify where the value sits
• Working knowledge of [your AI platform] and how enterprise deployments are governed
• Technically capable of integration work: APIs, connectors, authentication, and debugging when a workflow breaks

Desirable

• Experience in [your industry] or an adjacent regulated sector
• Scripting or development background, [Python or JavaScript]
• Experience running change or enablement at scale
• Security or compliance experience in an enterprise environment

Not required

• A machine learning or data science background. This is not a model development role.
• A specific number of years. The field is younger than that. Show us what you have built.

How we will measure success

• Agents in production with named business owners
• Weekly active use of what you have shipped
• Hours returned or cycle time reduced against a measured baseline
• Backlog health: use cases identified, delivered, and retired
• Agent estate under control: every deployment registered, owned and costed

Compensation and benefits

[Range, benefits, and whether you are open on seniority. State the range. The people you want are in demand and will not chase a salary you have hidden.]

Copy to here.

Part 3: Interview and hire

Where these people come from

Look in four places, in this order. The open market for this profile is thin: the people with the full combination of sector knowledge, enterprise credibility and hands-on build experience are mostly employed by the AI vendors and the firms that supply them, on compensation a non-tech enterprise will struggle to match. Assume you are building this person, not buying them.

Inside your own business functions. The person already building with AI because their job annoyed them enough. The finance analyst who rebuilt the month-end pack, the bid manager who automated first-draft proposals. They arrive with domain credibility, which takes an external hire a year to earn and is what gets a process changed. Their gap is engineering depth, and that is teachable.

Inside IT, on the delivery side. Solution architects, integration engineers and senior business analysts who have shipped systems people use. They have the technical range and the stakeholder muscle. Their gap is AI-specific build experience, and since nobody in the market has more than two years of it, a capable engineer closes that gap in months.

Consultancies and systems integrators. People who have delivered enterprise change and want to own an outcome instead of moving to the next client. Strong on method and stakeholder work. Check they have built recently and are not selling a deck.

AI-native companies. Strongest technically. Check they can operate in an enterprise: security review, change control, and a finance team that needs convincing. Some thrive on it, some find it intolerable.

Screening call, 30 minutes

1. Tell me about something you built with AI that other people use. Who uses it, how often, and what broke first?
2. Walk me through how you decided that was the right thing to build.
3. What have you built that failed, or that nobody adopted? What did you learn?
4. What did you change your mind about in the last six months?
5. Describe how you would explain an agent’s limitations to a sceptical department head.
6. What do you want from the organisation to do this job well?

Question one is the filter. Candidates who talk about AI in general terms rather than about a specific thing they shipped do not pass it.

The build exercise

Set this as a live 45-minute session, not a take-home. You are watching how they work, not grading a finished artefact.

Here is a real process from our [finance / HR / operations] team. Here is the input they start with and the output they produce. Build something with [your AI platform] that does a useful part of it. Talk us through your thinking as you go. You have 45 minutes and you can ask us questions.

Give them a real process with real constraints and a sanitised sample of real data. Then watch for:

• Do they ask what “good” looks like before building, or start typing?
• Do they check the output against the constraint you gave them?
• When the first attempt is wrong, do they debug it or start again?
• Do they say what the approach will not handle?
• Do they end with something that works, or something that demos?

The candidate who produces a partial solution and can tell you precisely where it breaks is a stronger hire than the candidate who produces a polished one and cannot.

Scorecard

Score each dimension 1 to 5. Agree the weighting with the panel before you interview, not after.

A candidate scoring 5 on build and 2 on stakeholder credibility will ship agents nobody uses. A candidate scoring 5 on stakeholder credibility and 2 on build becomes a coordinator. Weight the dimensions, do not average them away.

Reference questions

• What did they build that is still in use today?
• Who did they have to win over, and how did that go?
• What happened when something they shipped broke?
• Where did they need support, and what kind?
• Would you hire them again into a role with no established playbook?

First 30 days

• Week 1: Meet the sponsor and agree the first two functions. Get system access agreed, not requested. Meet security.
• Week 2: Discovery in the first function. Map the processes. Do not build anything yet.
• Week 3: Pick one use case with a named business owner and a measurable baseline. Build it.
• Week 4: Ship it to real users. Sit with them while they use it. Fix what you find. Report to the sponsor with the baseline and the change.

Shipping something real in month one buys the credibility to do the harder work in month four.

Implementation notes

Internal hire, external hire. Internal candidates arrive with the relationships and the domain knowledge, and need engineering support for the first six months. External candidates arrive with the technical range and need an internal partner to open doors. Whichever you choose, budget for the gap rather than assuming it closes on its own.

Scope the role so one person can win. Scope is where this hire fails: one person handed every function, every use case and every operational duty at once. Name the functions in scope and the ones that are not. Expand when the first ones are working.

Do not hire this role to invent your mandate. If the AI programme has no executive owner, no budget and no agreed use cases, a delivery hire cannot supply them. Fix the mandate first. The readiness test in part one is the checklist.

Give them an operating model. An AI Operations Lead who walks into a documented map of how the organisation works, which processes each unit owns and where AI is landing today, is productive a full quarter earlier. If that map does not exist, their first quarter builds it, and your first agent arrives a quarter late.

Introduce the role carefully. If the organisation reads this person as an auditor sent to find the jobs that can be cut, no amount of skill recovers it. Have the sponsor introduce them in writing as a resource teams can call on, and make the first delivery something a team asked for rather than something imposed on them. The first three engagements set how every later one is received.

Adjust for your industry. Regulated sectors need the security and compliance line weighted more heavily, and the 90-day plan stretched. Manufacturing, logistics and construction need someone comfortable away from a desk, because the highest-value processes are not in the office.