Head of Applied AI in City of London

Location: Our offices are in Central London, along with several of our customers. Travel to our customer sites is required, the frequency of which will vary from customer-to-customer. You need to be able to travel onsite to London up to 3 days per week, although onsite requirements will vary week-to-week and customer-to-customer from 0 to 3 days.

Salary: Competitive (DOE)

Defence/SC Clearance Requirements: SC clearance is required for our customer facing teams. You do not need active SC clearance as we will undertake checks upon you joining, but you must be eligible to pass SC clearance. DV clearance may be required for certain roles, so the ability/willingness to pass DV clearance is advantageous but not essential.

About Cloudscaler: Cloudscaler is a rapidly growing cloud consultancy. We partner with ambitious enterprise and public sector customers to design, build, operate, and support secure, governed cloud platforms that meet the highest regulatory and industry standards. We are scaling deliberately. Our revenue ambition depends on deploying the right people onto the right work quickly, and on making disciplined use of our capacity. This role is central to that.

About the role: Cloudscaler’s Applied AI business unit launched at the start of 2026 and has grown quickly, with an established offering, live client engagements and a pipeline in place. The current Head of Applied AI is stepping back from the role for personal reasons and remains with the business to hand over, so the incoming Head inherits a running unit rather than a standing start. We are recruiting to take it into its next phase of growth.

The work is running; the structure is not yet. Offerings are defined, collateral exists, engagements are live and Cloudscaler’s public voice on applied AI is established. What does not yet exist is the shape around it: there is no ring-fenced team, no separate P&L and no formally defined roles. The unit began with strategy, technical expertise and sales all held in one place. Technical capability has grown since; strategy and commercial ownership have not. Formalising the practice - defining the team, the roles and the commercial structure - is part of the job rather than something the incoming Head inherits finished.

This role has both external and internal responsibilities:

• Externally: It owns our AI go-to-market offerings and the customer, partner and pre-sales activity that turns them into pipeline, and it is Cloudscaler’s public voice on applied AI.
• Internally: It carries full commercial accountability for the business unit, provides technical delivery oversight, builds and leads the practice, and is accountable for enabling the whole company to work with AI.

The role reports to the CTO and works closely with leadership across the business. The person needs to be technical enough to direct and challenge the engineering work, and commercially credible enough to scale the unit and its offering. It is a hands-on role. In one week, you might write a business case for the Leadership Team, advise a client CTO on their AI strategy, review an engineer’s architecture decision and speak at a conference on what we have learned.

External responsibilities: These responsibilities are customer-facing, partner-facing and public facing:

• Relentless client focus - Maintaining a relentless client focus, calibrated to how mature the business unit is, and being decisive about where to concentrate effort and what to stop. Early on that means client meetings and pipeline growth; later it means shifting weight to larger, more sustainable areas of demand.
• AI go-to-market offerings - Defining and owning our AI go-to-market offerings, working with sales to take them to market. For each offering, defining what it is, who buys it, the problem it solves, its indicative price and its delivery model, and reviewing the set as the market moves, retiring what no longer sells and evolving what does.
• Customer relationships - Building trusted-advisor relationships with our customers, from CTOs and CDOs to engineering leaders, advising at every level and translating the same insight into the right language for a C-suite sponsor setting strategy and for the developers implementing it. Converting those relationships into repeat work and referenceable engagements.
• Pre-sales leadership - Leading pre-sales activity on AI opportunities, from qualification through to proposal. Supporting the delivery and commercial teams on proposals, workshops and pitches, and leading on bids where applied AI credibility is the differentiator. Qualifying opportunities in or out, including declining those we should not pursue.
• Client collateral - Developing reusable client collateral (opportunity assessments, roadmaps and business cases) grounded in real delivery experience. Keeping it current as the offerings and the technology move and making it available to the delivery and commercial teams rather than rebuilt for each deal.
• Partner and industry relationships - Building and owning our AI partner and industry relationships (AWS, Anthropic, OpenAI and relevant industry bodies), including the technical relationship, joint go-to-market activity, and achieving and maintaining the respective AI partner competencies that matter to our customers. An existing network inside our partners is a significant advantage, as is fluency in how each organisation works, where it sits in the market and what it is seeking to achieve commercially. Deciding which competencies to pursue and in what order and keeping them current.
• Thought leadership and public voice - Providing thought leadership on applied AI and establishing Cloudscaler’s public voice through speaking, meetups, blogs and whitepapers. Building our standing in external applied AI communities and contributing to industry discussion, with material grounded in work we have delivered.
• Market awareness - Tracking the AI market as it moves - model releases, tooling and emerging frameworks - and forming a clear view on what is credible, what is hype, and what Cloudscaler should adopt. Bringing that view to the Leadership Team early enough to act on it, and setting our position on what we adopt, what we monitor and what we set aside.

Internal responsibilities: These responsibilities cover the commercial, delivery and capability side of the business unit.

• Commercial accountability - Carrying full commercial accountability for the business unit, which is run as its own business and measured on clear financial metrics: pipeline, revenue and gross profit. In the first six months that means a six-month revenue plan, with forecast gross profit covering the cost of the leader within six months and the team gross profit positive within two quarters. This grows into full P&L ownership as the unit scales, including team economics that stand on their own.
• Reporting and investment cases - Reporting to the Leadership Team on monthly KPIs covering pipeline, utilisation and gross profit against the six-month plan, and building the investment cases that fund the unit’s growth. Reporting runs from month one, including the business case for establishing a dedicated team.
• Technical delivery oversight - Providing technical delivery oversight of AI customer engagements, working with our delivery teams. This is an architect’s role rather than an engineer’s: technical enough to hold your own with client engineers and to delegate the detail, and disciplined enough not to commit to capabilities we cannot feasibly deliver. Reviewing architecture and design decisions and acting as the decision-maker when a technical call is needed.
• Building and leading the practice - Building and leading our AI practice: owning Cloudscaler’s AI strategy and the roadmap to deliver it, hiring and line-managing the team as it grows, capability development, and a repeatable methodology and delivery standards that make quality consistent regardless of who is leading the engagement. Defining the capability model and establishing the practice as an internal centre of excellence for review and quality assurance as it grows.
• Decomposing and delegating ownership - Breaking broad areas of activity into distinct responsibilities that can be delegated and genuinely owned, and building the team to hold them. The scope of this role is deliberately broader than one person can carry: the expectation is that the Head of Applied AI grows a team around them - including the AI enablement hires already in train - and leads through that team rather than delivering everything personally. Internal AI usage guidance, reviewing internal projects, pre-sales attendance, marketing campaigns and enablement workshops each need a named owner; an informal approach works while the unit is small but will not scale.
• Accelerators and intellectual property - Building reusable AI accelerators and intellectual property that shorten and de-risk customer delivery. Registering the assets as the work matures - accelerators, frameworks and reference architectures - and working with engineering to keep them testable and current as models and tooling evolve. Treating them as Cloudscaler intellectual property with standalone value rather than delivery tools alone.
• Company-wide AI enablement - Enabling every Cloudscaler employee to work with AI, including defining our internal AI learning pathways. Setting the guidance for how AI is used in our own work and running the enablement activity that puts it into everyday use across the whole company rather than the practice alone.
• Internal community of practice - Growing an internal AI community of practice.