Top AWS Jobs in London

Own the architecture and hands-on development of Mastercard’s enterprise agentic AI platforms. Lead runtime orchestration, multi-agent coordination, model and tool integration, governance, security, multi-tenancy, observability, evaluation, and production lifecycle management. Establish technical standards, architecture governance, and API-first platform capabilities while mentoring engineers and advising senior stakeholders. Build scalable, secure, model-agnostic systems using cloud-native technologies and modern AI frameworks.

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.

Build and deploy production-grade, LLM-enabled applications for strategic enterprise customers. Own backend services, frontend integrations, data orchestration, vector databases, and AI pipelines while working onsite with customer teams. Develop reusable libraries, prompts, SDKs, scaffolds, and documentation; integrate legacy systems; debug distributed systems; optimize performance and observability; and influence the AI platform roadmap. Requires extensive software engineering experience, strong AI/ML tooling knowledge, cloud and DevOps fluency, and travel up to 30%.

Leads partner and field marketing across the UK and Ireland, with responsibility for IT buyer engagement, joint go-to-market programs, events, demand generation, partner sponsorships, budgets, vendors, and marketing development funds. Partners with sales, alliances, and global marketing teams to create pipeline, progress opportunities, measure ROI, and report partner-sourced and partner-influenced revenue. Requires B2B technology marketing experience, strong commercial judgment, CRM and marketing automation proficiency, and experience with technology partners.

Design, prototype, and evaluate machine learning models for travel products. Build maintainable training and inference code, participate in ML system and API design, prepare and analyze data, and collaborate with scientists, engineers, product partners, and mentors. Interns contribute to research and product experiments involving feature engineering, statistical methods, model evaluation, A/B testing, fairness, and production-aware AI/ML solutions.

Design, prototype, and evaluate machine learning models for travel products under experienced scientists’ guidance. Build maintainable training and inference code, conduct experiments and A/B tests, analyze and prepare data, perform feature engineering and error analysis, and contribute to ML system, API, and data design. Collaborate with scientists, engineers, product partners, and mentors to define metrics and develop robust, fair, production-aware AI solutions.

Own production system reliability, incident response, observability, automation, and recovery workflows. Build self-healing capabilities, diagnostic tooling, runbooks, and golden-path operational workflows while partnering with platform and engineering teams on CI/CD, infrastructure resilience, SLOs, and reliability improvements. The role combines hands-on production support with development of automation that reduces toil, accelerates recovery, and prevents recurring failures.

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.

Design, build, test, deploy, and operate mission-critical backend systems across the capital markets trade lifecycle. Translate business problems into measurable software outcomes, collaborate with product managers and stakeholders, and contribute to AI-enabled features. Own production reliability, monitoring, incident response, and quality while mentoring engineers and establishing technical standards. Work primarily with Kotlin, Spring, Postgres, Elasticsearch, Kafka, Kubernetes, and AWS in autonomous Kanban teams.

Build and deploy production-grade, LLM-enabled applications spanning backend services, data orchestration, vector databases, and frontend interfaces. Work directly with enterprise customers in agile sprints, integrate legacy systems, create reusable SDKs, prompts, libraries, and scaffolds, and influence AI platform strategy. The role requires full-stack engineering, distributed-system debugging, cloud deployment, observability, product judgment, and up to 30% travel for onsite customer engagements.

Design, build, and improve Wise’s scalable compute platform. Automate infrastructure and application processes using AWS and Kubernetes, enhance reliability and security, identify systemic improvements, and lead technical initiatives. The role includes hands-on engineering, cross-functional collaboration, mentoring, and advancing cloud-native capabilities such as Kubernetes operators and service mesh infrastructure.

Lead site reliability engineering initiatives for AWS platforms, improving reliability, scalability, observability, resiliency, and service-level performance. Conduct design reviews, lead major-incident response, establish SLOs and error budgets, reduce operational toil, and mentor engineers. Evaluate and govern AI-assisted reliability workflows while ensuring security, auditability, and data-sensitivity controls. Collaborate across teams on CI/CD, testing, automation, containerization, networking, and operational readiness.

Develop and maintain automated testing frameworks for cloud-native applications, covering integration, system, regression, performance, security, data quality, and UAT testing. Integrate tests into CI/CD pipelines, analyze results, track defects, collaborate with Agile teams, and review architecture for testability and scalability. The role also involves mentoring QA teammates and working with cloud platforms, microservices, databases, data workflows, containers, and orchestration technologies.

Develop scalable, cloud-native SaaS cognitive solutions on AWS using microservices, micro-frontends, serverless technologies, data pipelines, and machine learning components. Responsibilities include solution architecture, hands-on software development, AWS deployment, CI/CD, testing, documentation, technical guidance, security support, and collaboration with global engineering and domain teams. The role also involves designing data-intensive systems and supporting full product lifecycle management.

Leads customer workshops and designs secure, scalable cloud, infrastructure, storage, backup, and data protection architectures. Acts as technical lead for complex transformation initiatives, develops technology roadmaps, provides architectural governance, collaborates with delivery teams, and advises executive and technical stakeholders on modernization, resiliency, security, and disaster recovery strategies.

Manage AWS and Azure cloud partnerships to drive co-sell motions, marketplace transactions, qualified pipeline, and revenue growth. Build relationships with cloud partner sales teams, align regional go-to-market plans, educate partners, support enterprise sales cycles, and lead quarterly business reviews. The role requires strong alliance management, cloud marketplace, enterprise sales, communication, and cross-functional influence skills.

Build and maintain DevSecOps tooling for AWS cloud environments, including threat modeling, security guardrails, policy-as-code, vulnerability analysis, compliance reporting, and runtime security visibility. Develop automation for security findings and software supply chain controls, support audits and incident response, and provide remediation guidance to engineering teams. Collaborate with platform engineers, product squads, risk, and governance teams while using authorized AI-assisted development tools securely and validating generated outputs.

Designs and manages secure, automated CI/CD pipelines across AWS and GCP. Leads DevSecOps, GitOps, Infrastructure as Code, cloud security, container orchestration, monitoring, testing, and automation initiatives. The role develops scalable cloud-native architectures using Terraform, Ansible, Kubernetes, Docker, Helm, and related tools; troubleshoots infrastructure; ensures compliance and resilience; and collaborates with software, testing, security, and cloud engineering teams.

Designs, develops, tests, troubleshoots, and maintains secure, scalable software solutions within an agile Market Risk Technology team. Produces architecture and design artifacts, develops production code, analyzes large datasets, and improves system architecture, coding practices, resiliency, and automation. Uses enterprise-authorized AI-assisted development tools responsibly while validating generated code for correctness, performance, and security. Preferred experience includes Python, AWS, and data engineering.

Leads software engineering for JPMorganChase’s Market Risk Technology team, designing, developing, testing, and troubleshooting secure, scalable production systems. Responsibilities include code review, operational stability, automation, AI-assisted engineering adoption, responsible AI practices, vendor and architecture evaluations, and technical leadership. The role emphasizes Python, AWS cloud-native architecture, microservices, distributed systems, APIs, event-driven systems, data platforms, agile delivery, security, and financial services technology.

Build and deploy production-grade LLM applications spanning backend services, data orchestration, vector databases, and front-end interfaces. Work directly with strategic customers in embedded, field-based engagements; integrate legacy systems, iterate in agile sprints, and deliver scalable AI solutions. Create reusable SDKs, libraries, prompts, scaffolds, and documentation while influencing platform strategy, developer experience, and product roadmaps.

Leads second-line technology and information security risk oversight for Mastercard’s digital-asset strategy. Builds technology control frameworks, risk taxonomies, control libraries, governance routines, and continuous monitoring. Oversees blockchain infrastructure, custody, wallets, cloud environments, cryptography, and third-party dependencies. Independently challenges first-line assessments, validates controls, manages risk acceptances and remediation, and communicates material risks to senior governance forums.

Lead technology risk and cybersecurity control governance for a consumer banking line of business. Own security configuration management across AWS, data centers, endpoints, and applications; define standards, control objectives, metrics, and evidence requirements; oversee testing, remediation, audit readiness, and executive reporting. Partner with engineering, operations, architecture, and risk teams to assess threats, challenge control sufficiency, integrate security tooling with GRC, and improve security posture.

Designs, develops, tests, and maintains scalable software features and services for Expedia Group’s B2B Credit platform. The role involves Java/Kotlin development, RESTful APIs, microservices, cloud platforms, architectural collaboration, production troubleshooting, code review, and improving engineering practices. The engineer partners with product, design, and cross-functional teams to deliver reliable travel technology solutions.

Leads vulnerability intelligence, prioritization, detection, and response across customer environments in EMEA. Owns the vulnerability lifecycle, correlates threat intelligence and exploit data, operates security platforms, automates scoring and reporting, develops risk models, and tracks remediation outcomes. Produces dashboards, executive briefings, and intelligence reports; supports audits and penetration-test remediation; collaborates with engineering and customer teams; and delivers training on vulnerability management and security operations.