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 mandate is led personally by Adrian Lawrence FCA. For Chief AI Officer recruitment (C-suite level), see our Chief AI Officer recruitment service.

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.

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.

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