Cloud Computing Jobs

Amazon · London

Builder-first solutions architect role driving GenAI adoption for global energy and utilities customers, prototyping and delivering production-grade agentic AI platforms, copilots and reference architectures on Amazon Bedrock, AgentCore and partner tooling. Involves hands-on coding, whitepapers and influencing AWS product roadmaps as a trusted advisor to senior customer technical leaders.

Amazon · London

Drives AWS's European backbone network strategy, designing large-scale architecture and executing expansion plans with milestone tracking. Leads vendor strategy for wavelengths, dark fiber and submarine cable capacity, managing supplier onboarding, performance and commercial partnerships. Works with finance on budget planning and balances business needs against technical and cost constraints.

Nebius · London

Owns reliability, performance and observability of a large-scale inference platform serving text, vision and audio models. Day-to-day work spans telemetry pipelines, Kubernetes autoscaling, Terraform modules and request-routing resilience, with incident response and post-mortems. Requires deep Kubernetes, Prometheus, Grafana and IaC fluency plus GPU workload experience.

Databricks · London

Customer-facing, billable role building and productionising data and AI solutions on the Databricks platform. You own architecture and design decisions, implement end-to-end systems across data engineering, ML/AI and application development, and embed with client teams from ICs to executives. Involves around 20% travel to customer sites.

Databricks · London

Embed with strategic enterprise customers to design, build and productionise custom fullstack data and AI applications on the Databricks Data Intelligence Platform. You own architecture and end-to-end delivery across data engineering, ML/AI integration and user-facing interfaces, partnering with Sales, Product and Field Engineering. Expect up to 50% travel to client sites.

Nebius · London

Owns focused research projects on efficient LLM and VLM inference, spanning quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization. Builds prototypes in PyTorch, Triton, and serving frameworks, evaluates trade-offs across latency, throughput, memory, and cost per token, and works with engineering teams to move results into production.