Head of AI and Machine Learning Recruitment

Head of AI and Machine Learning Recruitment

Exec Capital provides senior Head of AI and Machine Learning recruitment for UK firms across tech and SaaS, financial services, healthcare and life sciences, professional services, and broader enterprise contexts. The Head of AI/ML role has emerged as a substantive senior appointment over the past few years as UK firms move from experimental AI work into production deployment at scale. The role typically reports to the CTO, Chief AI Officer (where one exists) or directly to the CEO, and carries accountability for AI strategy, model deployment and operations, AI governance and risk, and (often) the integration of AI capability into core commercial and operational processes. UK senior Head of AI hiring is shaped by the genuinely international candidate market, equity-heavy compensation expectations, and the substantial gap between AI engineering depth and commercial AI strategy capability — strong candidates typically need both.

Our practice covers UK senior Head of AI/ML appointments across SaaS scale-ups making AI core to product strategy, financial services firms deploying AI in production for credit, fraud, customer service and trading, healthcare and life sciences firms using AI in drug discovery and clinical contexts, and broader enterprise firms integrating AI into commercial and operational workflows. Every search is run by our specialist team, with Adrian Lawrence FCA overseeing each mandate. For Chief AI Officer recruitment (C-suite level), see our Chief AI Officer recruitment service.

This page is for the hire one level below the C-suite: the person who owns AI and machine learning delivery day to day, whether the title on the offer letter is Head of AI, Head of Machine Learning, Director of AI, AI Director or Head of Applied AI. It sets out what the role covers, how it differs from the neighbouring roles, where candidates come from, how the regulatory picture affects the brief in November 2026, how packages are usually structured, and how a search with Exec Capital runs from first call to start date.

Why Head of AI appointments go wrong, and how to avoid it

Head of AI appointments are particularly prone to one specific failure mode: hiring senior engineering depth without commercial integration capability, or hiring commercial integration capability without sufficient engineering depth. Strong Heads of AI need both — substantive technical credibility with the engineering team plus the commercial fluency to translate AI capability into business outcomes. The pattern that recurs is firms over-indexing on one dimension and producing senior appointments that struggle in the other. The fix is calibrating the brief explicitly to require both, and assessing both substantively during the search.

At Exec Capital we run Head of AI searches with both dimensions worked through at the front of the brief. Strong candidates evaluate firms carefully — the AI strategy, the existing engineering depth, the commercial use cases, and the substantive AI governance framework. Compensation is calibrated to the international AI talent market rather than UK-domestic norms. Every search is run by our specialist team, with Adrian Lawrence FCA overseeing each mandate, so the brief, the assessment and the offer are handled by people who understand both the technical and the commercial side of the appointment.

Three other problems come up often enough to plan for. The first is a title that does not match the job: a business advertises a “Head of AI” when what it actually needs is a hands-on ML engineering lead to build the first production pipeline, or the reverse. The second is a role with no clear owner of data: an AI leader cannot deliver if the data they need sits in systems nobody has agreed to clean, document or share. The third is an unresolved reporting line, where the Head of AI sits between a CTO who sees AI as an engineering capability and a commercial leader who sees it as a product, and neither has agreed who sets priorities. Settling these three points before the search starts does more for the outcome than any amount of interviewing afterwards.

How a Head of AI search works

Every Head of AI search follows the same sequence. We agree the timetable with you at briefing, because the right pace depends on the seniority of the role, how much of the market you want covered, and how quickly your own interview panel can meet candidates.

What the Head of AI/ML role covers

The substantive Head of AI role typically covers four dimensions.

AI strategy and roadmap. The firm’s AI strategy across product, operations, customer-facing applications and internal productivity. Strong Heads of AI bring substantive thinking about which AI use cases produce real commercial value versus which are experimental.

AI engineering and deployment. Building and operating production AI/ML systems — model development and training, MLOps and deployment infrastructure, model monitoring and retraining, and the broader AI engineering practice.

AI governance and risk. The substantive risk management dimension — model risk, bias and fairness, data governance, regulatory compliance (particularly in financial services and healthcare contexts), and the broader AI ethics framework.

Commercial integration. Working with the executive team to embed AI capability into commercial processes — product strategy, customer experience, operational efficiency, and (often) M&A activity in AI-related areas.

How the role has changed with generative AI

A few years ago most Head of Machine Learning roles were about building predictive models on a firm’s own data: credit scoring, churn prediction, demand forecasting, pricing. That work has not gone away, but the arrival of large language models has added a second, different workload. Many firms now need someone who can decide when to build, when to buy and when to fine-tune; who can put retrieval, evaluation and guardrails around third-party foundation models; and who can control the running cost of inference as usage grows. The best Heads of AI hold both sets of skills, or at least know how to hire and manage people who do. When we take a brief, one of the first questions is which of these two workloads dominates, because it changes the candidate pool considerably.

Typical team and scope

Scope varies a great deal by business. In a scale-up, the Head of AI may run a team of a handful of ML engineers and data scientists and still write code. In a larger enterprise or bank, the role may cover several teams across applied science, ML platform and MLOps, model validation and AI product management, with a budget for cloud compute and third-party models. Typical responsibilities include:

• Setting the AI roadmap and the portfolio of use cases, with clear criteria for what moves from pilot to production and what is stopped.
• Hiring and developing ML engineers, data scientists, research scientists and AI product managers.
• Owning the ML platform: experiment tracking, feature stores, deployment pipelines, monitoring and retraining.
• Running model risk management: documentation, validation, bias testing, explainability and incident handling.
• Managing vendor relationships with model providers and cloud platforms, and the cost of compute.
• Reporting to the executive team and, in regulated firms, to risk and audit committees on AI performance and risk.

Head of AI vs Chief AI Officer vs Head of Data Science vs ML Engineering Lead

These four titles are often used loosely, and getting the level wrong is one of the most common reasons an AI search stalls. The table below sets out how we distinguish them when we take a brief. Real organisations blur the lines, but the distinction between strategic ownership, delivery ownership and technical leadership holds up well.

A useful test: if the person will spend most of their time with the board deciding where AI fits in the business model, you are hiring a Chief AI Officer. If they will spend most of their time making AI work in production and leading the people who build it, you are hiring a Head of AI. A Chief Data Officer, which some readers confuse with these roles, owns data as an asset across the firm (quality, governance, architecture and access) rather than AI delivery; see our Chief Data Officer recruitment service if that is the gap. Where the AI agenda sits inside a broader technology remit, the hire may instead be a CTO with strong AI experience.

The candidate pool

Five pools recur. Sitting Heads of AI at peer firms. Most direct pool, with substantial sub-sector specialisation. Step-up candidates from Senior AI Engineering Manager or Principal AI Scientist roles. Natural growth pool. Senior research AI scientists transitioning to commercial roles. Common pool, particularly for firms where research depth is strategically central. Returning UK candidates from US AI firms. Substantive pool given the concentration of senior AI talent at US firms. Senior consulting partners from AI practices. Big Four AI practices, specialist AI advisory firms.

What each pool brings, and the risks

Each pool has a typical strength and a typical gap, and the brief should say which gaps you can live with.

• Sitting Heads of AI have done the job, but sector matters: a Head of AI from a consumer app may have little experience of model risk in a regulated lender, and the reverse. They are also the most expensive to move and the most likely to receive a counter-offer.
• Step-up engineering managers and principal scientists are often the best value and the most motivated, but need checking for breadth: have they set a roadmap, managed a budget and dealt with executives, or only delivered within someone else’s plan?
• Research scientists moving into industry bring depth that few others can match, which matters for firms building their own models. The risk is pace and pragmatism: production systems need to ship, be monitored and earn a return.
• Returning UK candidates from US firms often bring experience of AI at real scale. Package expectations, especially on equity, and timing of the move need early discussion.
• Consulting leaders are usually strong on strategy, stakeholder management and governance, and can be excellent in a first AI leadership hire. The question is whether they have built and run systems themselves, or advised others who did.

Most good shortlists draw on at least two of these pools. We also look beyond the obvious titles: some of the strongest Head of AI candidates currently sit in roles called Director of Data Science, Head of Decision Science, Principal ML Engineer or Head of AI Platform.

How we assess Head of AI candidates

Because the main failure mode is an imbalance between engineering depth and commercial capability, our assessment covers both explicitly, plus governance and leadership. We share our written assessment with you for every shortlisted candidate.

Engineering and technical depth

We test whether candidates understand how their systems were actually built and run: the architecture choices, the trade-offs between build and buy, how models were evaluated before and after release, how drift and failure were detected, and what the running costs were. Where your firm needs it, we arrange a technical interview with an independent senior practitioner so that non-technical boards are not relying on impressions.

Commercial integration

We ask candidates to walk through use cases they took from idea to measurable value, how they chose between competing projects, and which projects they stopped and why. Strong candidates can explain the business case for an AI system in plain English, in terms a CFO would accept.

Governance and risk

For regulated firms in particular, we look for practical experience of model risk management, documentation, explainability, fairness testing, data protection and incident handling, and of reporting to risk and audit committees. Candidates who treat governance as an afterthought rarely suit a regulated environment.

Leadership and hiring

A Head of AI usually has to build or reshape a team in a market where good ML engineers have choices. We assess how candidates have hired, kept and developed technical people, and how they work with product, engineering, data and compliance colleagues.

When to hire a Head of AI: permanent, interim or fractional

Most Head of AI appointments are permanent, because the role builds a team and a platform over several years. But permanent is not always the right first step. If you are still deciding how much to invest in AI, or need cover while a permanent search runs, an interim or fractional AI leader can be the better choice.

Other ways to hire senior AI and technology leadership: for the C-suite AI role on a permanent basis, see our Chief AI Officer recruitment service; for engineering leadership more broadly, see VP Engineering recruitment; and for flexible senior leadership across functions, see our fractional C-suite service. A common pattern is a fractional or interim AI leader for the first phase, followed by a permanent Head of AI once the use cases and budget are proven.

AI regulation and governance: what the role must handle

The regulatory position on AI has moved over the past year, and any Head of AI hired now needs to understand it. The summary below reflects the position in November 2026.

United Kingdom

The UK has no AI-specific law. The government has kept a regulator-led approach: existing regulators apply cross-cutting principles (safety, transparency, fairness, accountability and contestability) within their own remits, as set out in the government’s response to the AI regulation white paper. In practice that means data protection law, consumer law, equality law and sector rules all apply to AI systems, and a Head of AI needs to know which of them bite in your business.

In financial services, the FCA has said plainly that it does not plan to introduce extra regulations for AI, and relies on its existing framework, including the Consumer Duty and the Senior Managers and Certification Regime; see AI and the FCA: our approach. There is no AI-specific senior manager function. Accountability for AI use therefore sits within existing senior manager responsibilities, and firms need to be clear which senior manager owns it. A Head of AI in a regulated firm usually supports that senior manager with the controls, documentation and management information they need.

The government’s AI Security Institute, renamed from the AI Safety Institute in February 2025, focuses on the security risks of advanced AI. Its research is a useful reference point for firms building on frontier models, though it is not a regulator of individual businesses.

European Union

UK firms that place AI systems on the EU market, or whose systems’ outputs are used in the EU, can fall within the EU AI Act. The Act was amended by the AI Omnibus (part of the EU’s Digital Omnibus simplification package), which entered into force on 27 July 2026. According to the European Commission, the position is now:

• Prohibited AI practices have applied since February 2025.
• Article 50 transparency obligations (for example, telling people when they are interacting with an AI system, and marking AI-generated content) have applied since 2 August 2026.
• Obligations for high-risk systems listed in Annex III (such as AI used in recruitment, creditworthiness assessment, education and some biometric uses) apply from 2 December 2027.
• Obligations for high-risk AI built into products covered by EU product legislation listed in Annex I apply from 2 August 2028.

For a Head of AI, the practical work is an inventory of AI systems, a classification of each against the Act, and a plan for the documentation, risk management, human oversight and logging that high-risk systems will need. The deferral gives firms more time, not less to do, and it is sensible to hire someone who has done this kind of work before.

Compensation and Package Structure

Head of AI compensation is set by an international market rather than a UK-domestic one. Senior AI and ML leaders are recruited by US technology firms, AI labs, banks and well-funded scale-ups, and candidates compare offers across all of them. That has three practical consequences for the package.

• Equity matters. In scale-ups and venture-backed businesses, share options or growth shares are often a large part of the offer, and candidates will want to understand the cap table, vesting and leaver terms.
• Bonus design should reward delivery, not activity. The best-designed plans tie a portion of variable pay to AI systems reaching production and producing agreed outcomes, rather than to the number of pilots launched.
• Non-cash terms carry weight. Compute budget, freedom to hire, access to data, conference and research time, and flexible or remote working can decide between two otherwise similar offers.

We do not quote figures on this page because AI pay varies widely by sector, scope and funding stage, and a single number would mislead more than help. At briefing we give you a current view based on live conversations with candidates. For wider context, our C-suite salary guide sets out how senior packages are structured across the executive team.

What a Head of AI typically delivers in the first 90 days

A good Head of AI does not start by building models. The first three months are about understanding, prioritising and making a small number of early, visible gains. A typical shape:

First month: listen and audit

• Meet the executive team, product, engineering, data, risk and compliance leads to understand priorities and concerns.
• Take stock of every AI and ML system in use or in development, including third-party tools staff are already using.
• Assess the team, the ML platform, data quality and access, and current running costs.

Second month: prioritise and set governance

• Agree a short list of use cases with clear commercial cases and owners, and stop or pause projects that do not meet the bar.
• Put a basic AI governance framework in place: an inventory, risk classification, approval process and incident route.
• Identify the main hiring gaps and start recruiting for them.

Third month: deliver and report

• Ship or materially improve at least one production use case, with agreed measures of success.
• Present a 12 to 18-month AI roadmap with budget, headcount and risk position to the executive team or board.
• Set up regular reporting on AI performance, cost and risk.

We discuss these expectations with candidates during the search, so the person you appoint arrives with a realistic plan rather than a generic one.

Engagement and fees

We describe the structure here rather than the figures, which depend on the role and are set out in our terms when we take the brief.

• Retained search is our usual basis for permanent Head of AI appointments. The fee is a percentage of first-year remuneration, paid in stages through the search, which commits a dedicated team to mapping the market and approaching candidates who are not looking.
• Contingent terms, where a fee is payable only on placement, can suit less senior or more straightforward roles. For Head of AI searches they rarely give the same market coverage. Our guide to retained vs contingent executive search explains the difference.
• Interim and fractional AI leaders are engaged on a day-rate basis, with the rate agreed before introduction and invoiced as days are worked.
• What is included: the brief, market mapping, direct approaches, interviews and written assessments, interview design support, referencing, offer management, and contact through notice and early tenure.

Who we place

Exec Capital places Heads of AI, Heads of Machine Learning, AI Directors and similar senior AI leaders for:

• Technology and SaaS businesses building AI into their products, from Series A scale-ups to established software companies.
• Banks, lenders, insurers, payments firms and wealth managers using AI in credit, fraud, financial crime, pricing, claims and customer service, through our financial services executive search practice and our fintech recruitment work.
• Healthcare and life sciences organisations using AI in drug discovery, diagnostics and clinical operations.
• Private equity and venture-backed companies where investors want AI to drive value creation, often alongside other leadership changes.
• Professional services, retail, logistics and industrial firms applying AI to operations, forecasting and customer experience.

For a broader view of our work across AI roles at every level, see our AI executive recruitment page.

Looking for a Head of AI role?

If you are an AI or machine learning leader considering your next move, please register with us and look at our current opportunities. We treat every approach in confidence.

Frequently asked questions

What is Head of AI recruitment?

It is executive search for the senior leader who owns a firm’s AI and machine learning work: strategy, engineering delivery, governance and the commercial use of AI. The role usually sits one level below the C-suite, reporting to a CTO, Chief AI Officer or CEO.

What is the difference between a Head of AI and a Chief AI Officer?

A Chief AI Officer is a C-suite role focused on enterprise AI strategy, investment and risk appetite with the board. A Head of AI runs the function that makes AI work in production. Larger firms may have both; smaller firms usually start with a Head of AI.

Do you recruit Heads of Machine Learning as well as Heads of AI?

Yes. Head of Machine Learning, Director of ML, Head of Applied AI and AI Director roles are covered by the same practice. We confirm at briefing whether the job leans towards predictive ML, generative AI or both, because that changes the candidate pool.

How do you recruit an AI Director?

The same way as a Head of AI: a detailed brief, a mapped search across the main candidate pools, structured assessment of technical depth and commercial judgement, and managed interviews and offer. AI Director is often the title used in larger organisations or consultancies for the same level of role.

How long does a Head of AI search take?

It depends on the seniority of the role, the scope of the search and candidates’ notice periods, which for senior AI leaders are often long. We agree the timetable with you at briefing and keep you updated at each stage.

Does a Head of AI need to be approved by the FCA?

Not as such. There is no AI-specific senior manager function, and a Head of AI below the senior management level does not usually need FCA approval. Responsibility for AI use sits within existing senior manager responsibilities, so regulated firms should be clear which senior manager owns it.

Does the EU AI Act affect UK firms?

It can, if a UK firm places AI systems on the EU market or its AI outputs are used in the EU. After the AI Omnibus, high-risk obligations for Annex III systems apply from 2 December 2027 and for Annex I products from 2 August 2028, while transparency obligations have applied since 2 August 2026.

Can we hire a Head of AI on an interim or fractional basis?

Yes. Interim AI leaders work full-time for a fixed period on a day rate; fractional leaders work a set number of days each week or month. See our interim and fractional Chief AI Officer services, which cover senior AI leadership on these terms.

How do you assess technical ability if our board is not technical?

We structure technical interviews around real systems the candidate has built and run, and can involve an independent senior practitioner. Our written assessments explain the findings in plain English so your board can make an informed decision.

How are Exec Capital’s fees structured?

Permanent Head of AI searches are usually retained, with a fee based on first-year remuneration paid in stages. Interim and fractional appointments are on a day-rate basis. We confirm the terms in writing before the search begins.

Further reading

For Chief AI Officer recruitment (C-suite level), see our Chief AI Officer recruitment service. For Chief Data Officer recruitment (often paired with CAIO and Head of AI in firms structuring senior data and AI leadership), see our Chief Data Officer recruitment service.

For role-specific senior hiring guides, see our Chief AI Officer hiring guide, Chief Data Officer hiring guide, CTO hiring guide, and the rest in our Knowledge Centre. For tech and SaaS sector senior hiring specifically, see our Tech and SaaS Executive Hiring Guide. For engineering leadership alongside an AI function, see our Head of Engineering recruitment and Engineering Director recruitment services.

For UK AI policy and frameworks, see the DSIT, the AI Security Institute (formerly the AI Safety Institute), and (for AI in financial services specifically) the FCA guidance on AI deployment linked above.