Cloud Platform Engineer (Senior / Lead)

About the role We are looking for a hands-on Senior / Lead Cloud Platform Engineer to shape and run the multi-cloud foundation that our engineering, data and product teams build on every day. This is a senior individual-contributor / tech-lead role at the heart of a modern hybrid-cloud strategy. Our goal is to make cloud infrastructure effectively invisible and commoditised for internal teams: engineers should ship through self-service golden paths and a well-designed internal developer platform, without needing to understand the underlying cloud plumbing. You will treat platform capabilities as products, with clear APIs, paved roads, strong defaults and great developer experience. A defining part of this role is preparing our infrastructure for a future (ore present) where some "engineers" are AI agents. You will design platforms, guardrails and interfaces that let both human engineers and autonomous AI agents provision, operate and optimise infrastructure safely — and you will use AI agents heavily yourself to automate and optimise day-to-day platform work. Security is central, not an afterthought. You will help drive a zero-trust approach across identity, network, workloads and data, ensuring the platform is secure by default for humans and machine/agent identities alike. You will also work closely with our data function, helping design and optimise the data pipelines, data stores and large-scale analytics infrastructure (for example BigQuery and similar warehouses) that the business depends on. What you'll do Design, build and operate our multi-cloud and hybrid-cloud platform across at least two of the top-three providers (AWS, Azure and/or Google Cloud), plus on-prem/hybrid connectivity where needed. Build and own an internal developer platform and self-service "golden paths" that make cloud infrastructure feel invisible and commoditised for engineering, data and product teams; and their AI agents. Deliver everything as code: infrastructure-as-code, GitOps, reusable modules, CI/CD pipelines and policy-as-code guardrails. Leverage AI agents extensively to automate and optimise platform work - provisioning, cost and performance optimisation, incident response, remediation and documentation. Prepare the infrastructure for AI agents as first-class "engineers": safe machine identities, scoped permissions, sandboxes, approval workflows and audit trails so agents can provision and operate infrastructure within tight guardrails. Embed a zero-trust security model across identity, network, workloads and data for both human and machine/agent identities; secure by default, least privilege, secrets management and continuous compliance. Apply SRE practices - SLOs/SLIs, observability, capacity planning, resilience and blameless incident management - to keep the platform reliable and cost-efficient. Partner with data engineering to design and optimise data pipelines, data stores and large-scale analytics infrastructure such as BigQuery, including query, cost and performance tuning. Mentor engineers, set technical direction and champion strong platform and security engineering standards across the organisation. What you'll need to succeed: Essential requirements: Extensive hands-on experience designing, building and operating production cloud infrastructure at senior or lead level. Multi-cloud experience across at least two of the top three providers (AWS, Microsoft Azure and Google Cloud), including a recognised professional-level cloud certification for each of those two providers (for example AWS Solutions Architect / DevOps Engineer Professional, Azure Solutions Architect / DevOps Engineer Expert, or Google Cloud Professional Cloud Architect / DevOps Engineer). Strong background in modern hybrid-cloud architecture and connecting cloud with on-prem/edge environments. Deep infrastructure-as-code and automation skills (e.g. Terraform/OpenTofu, Pulumi, Ansible), GitOps and CI/CD, plus containers and orchestration (Docker, Kubernetes). Proven experience building internal developer platforms, self-service golden paths and platform-as-a-product to abstract away cloud complexity for engineering teams. Practical experience using AI agents / LLM-based tooling to automate and optimise infrastructure work, and interest in designing infrastructure that AI agents can operate safely. Strong security engineering mindset with hands-on zero-trust experience across identity, network, workloads and data — including secrets management, least-privilege IAM and machine/workload identity. Solid programming/scripting ability (e.g. Python, Go) and strong observability, reliability and cost-optimisation practices. Desirable requirements: Experience working as a Site Reliability Engineer (SRE) with SLOs/SLIs, error budgets and incident management. A third top-tier cloud certification, or specialist security/Kubernetes certifications (e.g. CKA/CKS). Significant data engineering experience: designing and operating data pipelines and data stores, and optimising databases and large-scale data infrastructure such as BigQuery (including query, cost and performance tuning). Experience preparing environments for autonomous/agentic workloads — sandboxes, scoped machine identities, approval workflows and audit trails. Experience in a regulated or fintech environment. Your approach to work: Pragmatic and hands-on, with a strong bias for automation and eliminating toil. Product mindset — you treat internal engineers (human and AI) as your customers and obsess over their experience. Security- and reliability-first, collaborative, and comfortable leading and mentoring. Important to know: Location: We have multiple offices across the UK. We have a new office in London which is becoming more central to where collaborate in person. We have a flexible working policy with a few days per week in the office. Right to Work: Applicants must already hold a legal right to work in the UK without time restrictions and without the need for future sponsorship. We are unable to provide Skilled Worker visa sponsorship.