Lead Cloud Engineer

Role overview

Provide technical leadership for cloud engineering projects spanning migration, platform engineering and AI enablement. The role combines hands-on engineering with client advisory work, delivery oversight, pre-sales contribution and mentoring, helping teams build reliable, scalable and cost-conscious cloud platforms.

Responsibilities

- Set technical direction for one or more client or internal project teams, acting as technical lead or individual contributor. - Define migration approaches, shared tooling, deployment standards and responsibilities across delivery teams. - Build infrastructure as code, CI/CD pipelines and containerised application platforms using tools such as AWS CDK, Docker, Kubernetes and Helm. - Support cloud-native and AI workloads, including vector databases, retrieval-augmented generation architectures and AI lifecycle infrastructure. - Introduce observability and automated cost-control practices across cloud estates. - Advise clients, contribute to proposals and bids, and identify opportunities for further cloud or data work. - Mentor cloud engineers and represent the cloud capability through events, technical writing or community activity.

Requirements

- Strong hands-on cloud engineering experience, including software development and efficient production code. - Experience with infrastructure as code, CI/CD, containers, Kubernetes and Helm. - Experience designing or operating cloud platforms for demanding client environments. - Knowledge of AI infrastructure, vector databases, retrieval-augmented generation or related modern data architectures. - Experience with observability tooling such as OpenTelemetry and cloud cost-management approaches. - Proven client-facing consulting experience, including stakeholder management, commercial constraints, SLAs, budgets and deadlines.

Nice to have

- Experience in regulated sectors such as financial services or online gaming. - Certified Kubernetes Administrator, professional-level cloud architecture certification or FinOps certification. - Specialist credentials in generative AI, machine learning engineering or AI engineering.

Benefits and work setup

Flexible hybrid or remote working is available, with occasional travel to client sites or team offices. The package includes 35 days of leave including bank holidays, private medical insurance, enhanced parental and adoption leave, financial coaching, a 5% pension match and 40 hours of paid learning and development.