Top AWS Jobs in London

Lead engineer responsible for architecting and scaling fiat-to-crypto payments: bank and card integrations, real-time risk and compliance, double-entry ledger reconciliation, hot-wallet operations, idempotent APIs, event-driven exactly-once financial flows, and observability using OpenTelemetry and Grafana.

Design, scale, and operate multi-region, high-availability cloud infrastructure; lead incident response and on-call rotations; build automation and tooling in Python/Go; enforce risk, security, and operational standards; mentor teams and drive infrastructure architecture decisions.

Design, build and deploy production-grade ML systems for retail and consumer clients. Lead technical scoping, architecture and best practices, collaborate with data scientists and engineers, operationalise models, and advise customers to deliver scalable, secure ML solutions that drive business outcomes.

Lead analytics for NBCUniversal entertainment campaigns, focusing on incrementality (MMT), econometrics, and predictive modelling. Collaborate with US advanced analytics and local markets to deliver campaign reviews, translate results into actionable recommendations, assess data readiness, support matched market selection, and help integrate learnings into local practices.

Lead a small AI-focused engineering team to design, build, and deploy production-grade AI-driven features and services. Contribute hands-on to coding, debugging, testing and integrating ML models into scalable microservice architectures. Define technical direction, tooling, standards, and mentor engineers while collaborating with Product, Architecture, and Data teams.

Lead design and delivery of FinOps products across Onyx cloud platforms: enable cost visibility, optimization, governance, reporting, and training by partnering with engineering, finance, and R&D stakeholders in an agile environment.

Design, build, and support Kotlin/Java backend services and APIs for healthcare image management. Own subsystems through full lifecycle, follow engineering standards, participate in code reviews and QA, work with DICOM/HL7 integrations, and support deployments using cloud, containerization, and CI/CD. Produce technical documentation and collaborate with stakeholders.

Lead technical pre-sales for enterprise AI: engage C-suite, design secure multi-cloud AI architectures, run workshops, build POCs, present demos, influence commercial strategy, and ensure smooth handover to delivery.

The Director of Science will guide scientific research in genetics, leading algorithm development and managing a team to enhance MyHeritage's DNA products, impacting user ancestry discovery.

The role involves designing, developing, and maintaining high-throughput systems for the billing platform, collaborating with teams, leading technical discussions, and mentoring other engineers.

The L3 SOC Analyst leads complex incident investigations, drives automation, mentors junior analysts, and enhances SOC capabilities using advanced security technologies.

Design, implement, and maintain Golang infrastructure services for Confluent Cloud. Ensure availability, performance, and capacity planning of cloud systems while collaborating with engineering teams.

Design and maintain Golang infrastructure services for the Confluent Cloud Platform, ensuring performance, availability, and capacity planning of the service.

The Senior Sales Engineer leads new business acquisition by engaging prospects, promoting cybersecurity solutions, conducting assessments, and delivering tailored technical demonstrations.

Build and optimize scalable data infrastructure and pipelines for large time-series datasets, manage high-performance databases, and deliver actionable datasets and tools to support trading strategies and PnL-driven decision-making.

Embed with top enterprise accounts to deliver end-to-end production deployments of an agentic AI platform, building bespoke integrations, data pipelines and workflows, leading POCs, mentoring FDEs, and translating customer needs into product and engineering outcomes.

Design, develop, and maintain scalable Python backend systems and APIs. Lead end-to-end development, optimize performance, participate in architecture decisions, conduct code reviews, resolve production issues, and mentor junior engineers.

Design and build scalable Python backends and APIs, integrate and deploy LLM-powered applications (chatbots, summarization, semantic search), optimize model performance and costs, develop data pipelines for training and inference, ensure code quality and CI/CD, collaborate cross-functionally, and mentor junior engineers.

Lead architectural design and engineering build-out of company-wide MLOps and agentic AI platforms. Develop and productionize ML/LLM models, RAG, multi-agent systems, and GNNs; implement training, evaluation, serving, feature/vector stores, monitoring, and CI/CD; set engineering standards; collaborate with data science and senior stakeholders; represent company externally.

Lead solution design and architecture for corporate and SaaS platforms (People, Finance, Procurement, Legal, etc.), develop target architectures and integration approaches, work with vendors and cross-functional teams, contribute to enterprise architecture governance, and ensure solutions meet business, security, regulatory, and operational requirements.

Lead governance, risk and compliance activities to achieve and maintain security certifications (ISO27001, Cyber Essentials, PCI). Audit controls, perform gap analysis, remediate risks, support third-party security due diligence, and strengthen AWS cloud security policies while partnering with stakeholders to embed security training and continuous improvement.

Build and ship production-grade agentic AI systems and LLM-powered features. Architect RAG pipelines over sensitive financial data, contribute shared AI infrastructure, own production reliability and safety, and collaborate with product and design to deliver customer-focused outcomes in a regulated environment.

Build and operate production agentic AI systems and LLM-powered features. Implement RAG pipelines, extend shared AI infrastructure, monitor and debug production models, and collaborate with product and design to deliver secure, reliable AI capabilities in a regulated financial environment.

Design, build, and operate production-grade agentic AI and LLM-powered features and RAG pipelines over sensitive financial data. Collaborate with product and design, contribute shared AI infrastructure (LLM services, orchestration, evaluation), and own reliability, security, auditability, and compliance in production.

Lead the design and build of a greenfield analytics platform: extract reliably from a live Postgres product DB, handle schema drift, create operational data stores and a semantic layer, define canonical metrics, implement pipelines, dashboards (internal and customer-facing), set up AWS data infrastructure, and document systems for AI coding agents. Own architecture, tooling choices, data quality, observability, and a clean handover.