Top AWS Jobs in London

Lead design and delivery of production-grade AI/ML and agentic solutions for the LLM Suite platform. Own architecture, implementation, and operational stability using public cloud services and agentic frameworks. Build GenAI services, integrate LLMs, ensure secure high-quality code, and improve platform scalability and reliability through collaboration and communities of practice.

Design, build, and operate cloud-native lakehouse and streaming data platforms and pipelines using Python, PySpark, dbt, Airflow, and orchestration/infrastructure tooling. Ensure performance, reliability, security, and observability; model and transform data for analytics; deploy with IaC, containers, and Kubernetes; mentor engineers and drive platform standards and best practices.

Lead execution research and transaction cost analysis to inform algorithm roadmap. Build and govern TCA frameworks, run market-microstructure research and A/B tests, develop pre- and post-trade models, translate findings into production features, and partner with Product, Trading, and Technology while mentoring junior researchers.

Lead reliability, performance, and automation of analytics pipelines and reporting. Define SLIs/SLOs, monitor data quality/lineage, enable self‑service dashboards, enforce data risk/controls, drive FinOps/capacity insights, lead stakeholder reviews, and mentor junior engineers.

Build, operate, and scale LLM inference infrastructure with strong SRE practices. Develop backend services, deploy models on EKS/SageMaker and local GPU clusters, implement observability (Prometheus/Grafana/Alertmanager), optimize GPU performance and costs, lead incident response and reliability engineering, and automate remediation to improve platform stability.

Lead and perform hands-on penetration tests across applications, infrastructure, and cloud; design assessments and simulations; evaluate controls; produce detailed findings and remediation guidance; collaborate with cross-functional teams and apply threat intelligence to improve security posture.

Design, build and operate cloud-native backend microservices (Java) for banking products. Ensure secure, reliable, scalable systems with strong testing, performance tuning, zero-downtime releases, and production support including incident management.

Lead design and delivery of production-grade AI/ML solutions for Market Operations. Manage and mentor a team of data scientists, partner with AI engineering and stakeholders, ensure robust MLOps, model governance, deployment, monitoring, and documentation while driving measurable operational impact.

Design, build, test, and maintain scalable ELT data pipelines and architectures using Python and Databricks. Ensure data security, entitlement controls, and SDLC quality (CI/CD, testing). Use SQL/NoSQL, update data models, apply enterprise-authorized AI responsibly, and support controls and auditability across the data lifecycle.

Lead Markets Treasury optimization of liquidity, collateral, and capital across trading desks. Develop optimizers, analytics, and AI-enabled tools; review counterparty portfolios; influence methodology changes; partner with trading, technology, vendors, and clearing houses to maximize firm liquidity and capital efficiency while addressing regulatory constraints.

Lead design, build, and operate cloud-native AWS microservices using Kubernetes. Drive CI/CD, containers, IaC, monitoring, reliability, and incident resolution. Champion AI-assisted engineering practices, mentor engineers, participate in architecture reviews, and optimize platform availability and scalability for low-latency trading.

Lead design and deliver cloud-native backend microservices with reliable, secure, and scalable architectures. Build and test end-to-end solutions, operate production systems, drive AI-assisted engineering practices, coach engineers, manage stakeholders, and ensure high availability, performance, and secure data handling across the product lifecycle.

Lead development of Java/Spring Boot microservices for J.P. Morgan Personal Investing. Design and evolve cloud-native, event-driven architectures on Kubernetes using Kafka, deliver features from estimation to production, write tests, participate in code reviews, maintain monitoring/alerting, and drive adoption of enterprise-authorized AI-assisted engineering practices and SDLC automation.

Design, develop, and refine end-to-end data models and prototypes for financial planning and analysis. Support cloud data lakehouse solutions, ETL/ELT pipelines, dashboard/cube consumption, and help embed AI/LLM tools into workflows while collaborating with finance, analytics, and technology teams.

Lead development and architecture of KDB-based data systems for real-time and historical market data. Design, build, test, and operate resilient, high-performance KDB applications; drive SDLC improvements, CI/CD, and automation; mentor and lead teams, capture requirements, and partner with stakeholders and support teams during AWS migration and greenfield projects.

Lead strategy and delivery of AI-driven products for banking operations, identify high-impact AI use cases, own roadmaps and PRDs, manage cross-functional teams through launch, and drive stakeholder alignment to achieve measurable business outcomes.

Lead design and delivery of scalable GenAI platforms and services: build RAG pipelines and agents, model/version management, monitoring and drift mitigation, cloud-native microservices and deployment, testing and guardrails to reduce hallucinations, ensure compliance, and mentor engineering teams.

Design, build, and operate a high-throughput, low-latency real-time data platform using Flink and Kafka. Develop Java and .NET processing and APIs, manage cloud infrastructure (AWS, Terraform), implement CI/CD (Harness), and ensure observability, scalability, performance, and cost-efficiency across production systems.

Maintain and optimize cloud infrastructure for scalability and performance, build monitoring/alerts, participate in incident response and RCA, automate operations, tune databases and Kubernetes, create disaster recovery plans and documentation, and collaborate cross-functionally to improve reliability.

Design, build, and deploy production-grade generative AI and multi-agent systems for financial services. Architect scalable MLOps pipelines, optimize LLMs and multi-modal models, implement RAG and prompt-engineered agent workflows, and collaborate with clients and cross-functional teams to accelerate GenAI adoption and deliver measurable business impact.

Design, implement, and deploy customer-specific video platform integrations and infrastructure. Ingest and transform large data sets, build automation and CI/CD pipelines, ensure production readiness, and provide third-line support. Collaborate with architects, PMs, global engineering, and customers throughout solution lifecycle, emphasizing scalability, reliability, and operational sustainability.

Manage and lead a team of Technical Account Managers, supporting customer success and growth in EMEA. Provide technical guidance and thought leadership while collaborating with various teams to enhance services.

Lead EMEA partner marketing for cloud service providers and channel partners: design and run webinars, demand-gen, enablement, events, and social campaigns; coordinate cross-functional strategy and field alignment with AWS, Google Cloud, and Azure; measure and report ROI and OKRs; present results to leadership; and build Datadog’s EMEA presence at major partner conferences. Requires hybrid office attendance and some travel.

Join Markets Technology Trading as a Software Engineer II to design, develop, and troubleshoot secure, stable, high-quality Python code. Leverage enterprise AI-assisted development tools, collaborate in an Agile team, handle data processing and production engineering tasks, and apply SDLC practices to build performant, resilient applications while learning domain systems and secure coding standards.

The Senior Technical Support Engineer will provide expert support for Circle's API products, resolve technical issues, manage escalations, improve support processes, and contribute to the team’s goals for customer experience and operational excellence.