Top AWS Jobs in London

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead technical pre-sales for Twilio's Communications Platform across EMEA: run discovery, demos, POCs, architecture design, RFP responses, and enablement. Build trusted advisor relationships, present solutions, and collaborate with sales, partners, and services to win strategic accounts.

As a Senior DevOps Consultant, you will deliver complex projects, design CI/CD pipelines, and optimize cloud solutions, while mentoring engineers.

Build and operate Grafana Labs' bare-metal platform: provision storage, networking, and Kubernetes clusters; manage cluster networking, scheduling, autoscaling; maintain Crossplane compositions and Terraform modules; collaborate with application teams and participate in on-call rotation to ensure reliable, scalable platform operations.

Design, build, and operate AWS infrastructure and CI/CD pipelines across multiple environments. Support React front-ends, microservices, Lambda, and Kafka integrations. Implement IaC, secure networking (Transit Gateway, PrivateLink), IAM/MFA, API gateway (Kong Enterprise), FinOps tagging, monitoring, and troubleshooting. Work with multidisciplinary Agile teams to deliver secure, accessible digital services and support cloud governance and operational excellence.

Lead and grow a team of Presales Engineers for the DACH region, partnering with Sales and cross-functional teams to set technical strategy, enable scalable programs, and accelerate the sales funnel. Drive hiring, coaching, capacity planning, and data-driven decision-making while resolving customer blocking issues and engaging with Product, Engineering, and other stakeholders to ensure successful customer outcomes.

Build and maintain high-throughput, reliable database integrations to replicate data into ClickHouse. Debug production issues, improve observability and reliability, participate on-call, collaborate with customers and cross-functional teams, and influence product roadmap.

Build and extend the platform layer: design shared backend services and APIs, improve reliability and correctness, collaborate with product teams to remove platform bottlenecks, mentor engineers, shape architecture and migrations, and write high-quality fullstack code with a backend focus.

The Graduate Data Scientist will support data advisory engagements by documenting data processes, constructing architecture diagrams, and communicating strategies to stakeholders. They will utilize databases, SQL, and cloud platforms.

Design and own machine learning platforms for pricing technology, streamline workflows, and ensure reliable model performance at scale.

Build and operate backend integrations connecting the platform to corporate banking, e-invoicing, and tax systems. Design multi-tenant, reliable, auditable integration abstractions handling auth (mTLS, OAuth), ISO 20022/MT formats, retries, webhooks, and end-to-end reconciliation. Ensure data integrity across external partner APIs and production systems.

Design and build reliable data models and exposures using dbt and Snowflake, leveraging AI-assisted workflows. Own data quality, tests, contracts, and observability. Integrate third-party sources, create secure data shares, enable stakeholders with self-service BI, and continuously improve platform tooling and AI-driven processes.

Build scalable backend services and APIs with Java and Spring Boot on AWS. Design service interfaces and architectures, write clean testable code, implement automated/API testing and CI/CD, leverage AI tooling to improve features and workflows, and collaborate across engineering and product teams. Front-end experience with React/TypeScript/Next.js is beneficial.

Design, build, and deploy secure, scalable cloud infrastructure and reusable IaC for AI/ML workflows. Enable ML engineers/data scientists via CI/CD, GitOps, containerization, and platform libraries while partnering with stakeholders and researching open-source tooling.

Build and deploy production-grade ML software, tools and infrastructure; create reusable, scalable ML solutions; collaborate with engineers, data scientists and commercial leads; lead technical scoping and architecture; define deployment standards; advise clients and stakeholders on ML feasibility and delivery.

Lead design and delivery of production-grade ML and GenAI solutions on the Databricks platform. Architect end-to-end ML pipelines, optimize training/inference, implement MLOps/LLMOps, build MVPs for sales, influence product roadmap, mentor peers, and present technical content externally.

Develop and maintain scalable fullstack software systems using Typescript, React, and Node. Collaborate on web application architecture and coach junior developers.

Design, implement, and maintain stable, highly available infrastructure. Build and support CI/CD pipelines, deploy enterprise projects on AWS, automate build/release and monitoring, troubleshoot performance, participate in on-call rotation, and ensure security and compliance while collaborating across teams.

Provide Level 2 technical support for enterprise market-data and infrastructure products; perform root-cause analysis, escalate to engineering, own customer technical inquiries, contribute to support tooling and knowledge management, and participate in scheduled on-call rotations for a global client base.

Develop and maintain Risk Intelligence KYC products (World-Check One/Verify). Implement and improve API Gateway services, migrate services to cloud (AWS), build secure, highly available and scalable microservices, collaborate with cross-functional teams, use agile practices, CI/CD and monitoring, and support production systems.

Define and assure solution architecture artifacts, lead engineering teams as an SME, establish architectural guardrails, support senior leaders with cost estimation and planning, and collaborate cross-functionally using agile practices to deliver cloud-based applications and services.

Lead observability strategy and delivery for Services Technology, building end-to-end payment monitoring, telemetry, SLOs, dashboards, and integrations across on-prem and cloud. Partner with SREs, developers, and central platform teams to reduce toil, improve MTTD/MTTR, embed telemetry, and drive adoption of observability tooling and AI/ML insights for critical payment flows.

Operate and maintain customer cloud environments with a focus on uptime, security, and performance; provision and update environments; monitor using Azure tools; respond to incidents and on-call duties; drive automation, cost optimization, documentation, and service reliability against SLAs.

As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure to support data-driven decision-making in a collaborative environment.

Lead data architecture and strategy across OLTP, OLAP, and AI systems. Drive AI-first foundations (LLM orchestration, RAG, real-time inference), ensure DataOps/CI-CD, mentor technical leads, run architectural reviews, partner with product and engineering leadership, and optimise platform security, observability and costs.