Top AWS Jobs in London

Lead WAM data architecture and cloud transformation engagements: define enterprise data strategies, design canonical WAM data models, architect cloud-native data platforms, integrate core WAM systems, establish data quality and control frameworks, and advise senior stakeholders on technical-to-business translation.

Lead a global SOC/Attack Analysis team providing 24/7 monitoring and incident response. Manage SOC analysts, guide alert triage and case analysis, track metrics, improve detection/tuning, coordinate incident workstreams, and prepare senior management communications. Coach and develop team members and collaborate with cross-functional cyber teams and cloud stakeholders.

Lead cloud security architecture for AWS and GCP: embed threat modeling and architecture reviews, design landing zones and automated guardrails, map controls to standards, support audit/regulatory requests, evaluate emerging tech and AI-assisted security, and influence security-first practices across product and engineering teams.

Design, build and support cloud-native, microservice back-end applications using JVM languages. Deliver end-to-end solutions, write comprehensive tests, manage production incidents, and apply enterprise-authorized AI-assisted development tools while following secure and responsible AI practices.

Lead SRE responsible for improving reliability, observability, and operability of customer-facing microservices. Build automation and tooling to reduce toil, define SLOs/SLIs/error budgets, implement resiliency/self-healing patterns, drive performance testing and capacity planning, partner across teams, and govern safe AI-assisted engineering practices.

Lead networking platform engineering for cloud-native systems: design resilient, secure networks on GCP/AWS, build and maintain Kubernetes operators (Golang), improve observability/SLOs, troubleshoot production incidents, drive AI-assisted engineering practices, vendor evaluations, and lead engineering communities and agile ceremonies.

Lead backend engineering efforts to design, build, operate and secure cloud-native microservices. Deliver reliable, tested, scalable systems, manage stakeholders, coach engineers, support production incidents, and drive adoption of approved AI-assisted development practices.

Lead development of large-scale, real-time and historical market-data applications using KDB/Q. Drive system design, coding, performance optimization, scalability, high availability, and CI/CD automation. Mentor team members, manage delivery, partner with stakeholders and support teams, and help migrate and re-architect systems to AWS while improving operational stability and testing practices.

Lead DevOps Engineer on an agile team supporting low-latency electronic trading, data analytics and quant environments. Provide technical leadership, develop and review production code, manage CI/CD and release activities, automate tooling, monitor capacity, and deliver third-level production support while advocating firmwide engineering practices.

Design, build, and productionize autonomous and assistive multi-agent AI systems. Implement RAG and semantic search with vector databases, integrate tools/APIs, apply prompt engineering and guardrails, orchestrate microservices workflows, instrument agents for metrics, run evaluations/A-B tests, and mentor teammates.

Lead platform engineer driving cloud-native microservices, developer experience tooling, IaC, CI/CD migrations, and adoption of AI-assisted engineering. Designs architectures, writes secure production code, troubleshoots systems, and mentors teams to improve reliability and delivery.

Design, build, and deploy scalable ML services combining NLP, CV, and statistical methods. Collaborate with engineers, cloud and SRE teams to productionize models, ensure observability and compliance, and maintain models over time. Communicate results to stakeholders and document processes for regulatory needs.

Lead technical design and development of AI-native systems for credit pre-trade markets. Build scalable AI platforms, modernize legacy systems, deliver secure production code, partner with trading and product teams, support production operations, evaluate vendors, and drive engineering best practices and communities of practice.

Design, develop, deploy, and maintain advanced AI products and data pipelines. Collaborate with engineers, data scientists, and product owners to deliver production-grade, secure, scalable code. Architect AI-enabled engineering workflows, improve SDLC automation, ensure compliance, and provide technical leadership and architecture governance.

Lead design and delivery of enterprise-grade ML systems and production architectures. Build scalable, distributed, data-intensive applications using MLOps and cloud infrastructure (AWS, Kubernetes). Translate research into deployable ML solutions, implement automated pipelines, leverage PyTorch/NumPy and higher-level interfaces, collaborate with SRE/cloud teams, and mentor or optionally manage team members.

Lead DevSecOps architecture for CI/CD pipelines and cloud-native applications: embed automated security controls, champion IaC and policy-as-code, run threat modeling and architecture reviews, deploy preventive/detective guardrails, support audit/regulatory needs, and enable security-first culture using enterprise AI-assisted tooling.

Lead design and deliver scalable OMS and equities software using Java 17+. Build and maintain front-end UIs with JavaScript and modern frameworks (React/Angular). Drive adoption and governance of enterprise AI-assisted engineering practices, troubleshoot complex distributed/multithreaded systems, participate in code reviews and deployments, and collaborate across teams to ensure secure, high-quality production code.

Lead design and delivery of high-performance Java back-end systems for OMS/Sales. Provide technical direction, write and review secure production code, drive adoption and governance of AI-assisted engineering practices, ensure cloud-native (AWS/Terraform) scalability and operational stability, and promote SDLC best practices across teams.

Lead design, delivery and maintenance of on-prem and cloud market-data distribution systems (real-time, historical, AI/ML use cases). Collaborate with product/stakeholders, provide 3rd-level operational support, validate AI-assisted recommendations, ensure resiliency, security, auditability and strong process controls.

Lead architecture and roadmap for platform engineering; mentor engineers; design and deliver secure, scalable cloud-native infrastructure; ensure production stability (You Build It, You Run It); automate operations and implement DORA/SLIs/SLOs; drive FinOps and performance tuning; adopt and govern agentic AI-enabled engineering workflows with security and validation guardrails; collaborate with product, business, and security stakeholders.

Lead design and delivery of cloud-native data and AIML production systems. Build data engineering, backend, Cloud infra/DevOps and MLOps stacks, produce secure production code, architect microservices and distributed, data-intensive applications, and drive adoption/governance of AI-assisted engineering practices while communicating with stakeholders.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Lead a technical area driving design, development, and delivery of AI/ML and data platform solutions. Own architecture, governance, and adoption of AI-assisted engineering practices, ensure operational stability, and influence stakeholders while hiring and developing engineering talent.

Lead strategy and delivery of agentic AI across the Commercial & Investment Bank: design, build, and productionize multi-agent systems and RAG pipelines; integrate tools and cloud deployments; implement evaluation, safety, and observability; partner with stakeholders to deliver business impact; and mentor teams on LLMs, agent frameworks, and best practices.

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.