Design the Platform Behind Every Product, Data Insight and AI Innovation
As a sole contributor, you'll Lead the design and operation of a modern multi-cloud platform that underpins engineering, data and product teams across the business.
You will:
- Create self-service platforms, golden paths and developer tools that enable teams to ship quickly without managing cloud complexity.
- Build AI-ready infrastructure, guardrails and automation that support both human engineers and autonomous AI agents.
- Champion a zero-trust, secure-by-default approach across identities, networks, workloads and data
- Partner with data teams to optimise cloud data platforms, pipelines and analytics environments that power business-critical insights.
- 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.
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.
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.