cloudscaler - Head of Applied AI

• 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. • 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. • 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. • 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, giving colleagues outside the team a route to learn and contribute. Running it as a standing forum rather than ad-hoc sessions, so that colleagues outside the practice can build AI capability alongside their own work. • Candidate profile • This is a senior role combining technical depth with commercial ownership. We expect candidates to demonstrate the following: • Technical depth in applied AI - Current, hands-on understanding of AI technologies, models and tooling, sufficient to direct development work, review architecture and design decisions, and hold credible technical conversations with client engineers and architects. • Commercial track record - Experience owning a revenue or gross profit target, building the pipeline behind it, and reporting against a plan. Comfortable being measured on financial outcomes rather than activity. • Consulting and client credibility - A strong communicator who can advise at C-suite level and at engineering level, and whom clients want in the room on their most significant decisions. • Practice or capability building - Experience building a team or capability from a small base: hiring, setting delivery standards, and creating repeatable methodology rather than relying on individual heroics. • Partner ecosystem experience - Familiarity with the AWS and Anthropic ecosystems, or a comparable hyperscaler and model-vendor landscape, including how partner competencies and joint go-to-market activity work in practice. • External profile - A track record of public contribution on applied AI, or the clear appetite and ability to build one. • Leadership and delegation - Experience leading and line-managing a technical team, including setting direction, delegating genuine ownership and developing people. Comfortable achieving outcomes through a team rather than personally delivering the work. • Judgement and prioritisation - The ability to decide where to concentrate a small team's effort, and the discipline to stop work that is not producing.