Top AWS Jobs in London

The Senior Backend Developer will drive impact by collaborating with stakeholders, design and develop high-quality software, shape architecture, and lead technical direction while promoting innovation and collaboration within the team.

Design and deploy production data pipelines on AWS, including ingestion, processing, curation, data warehouses, and data lakes. Extract and integrate structured and unstructured data from multiple sources using APIs, scripts, SQL, and AWS-native or custom programming. Collaborate with clients, stakeholders, and cross-functional agile teams to deliver scalable cloud data solutions and solve complex business problems.

Designs, deploys, maintains, and improves enterprise infrastructure, primarily private cloud and Linux environments. Responsibilities include architecture, connectivity, upgrades, patching, application and infrastructure support, business continuity and disaster recovery, critical incident response, and internal project delivery. The role emphasizes Infrastructure as Code, automation, DevOps tooling, cloud platforms, enterprise Linux administration, and continuous service availability improvements.

Designs, builds, and operates highly available multi-cloud infrastructure across AWS, Azure, Kubernetes, and on-premises environments. Owns networking, infrastructure-as-code, CI/CD, observability, disaster recovery, and SRE practices for a globally distributed trading platform. Develops AI-assisted operational workflows for incident response, automation, and toil reduction. Mentors senior engineers, serves as a cross-team design authority, and contributes to the platform roadmap in a regulated, availability-critical environment.

The Principal Full-Stack Engineer will manage complex projects, improve existing features, write tests, and oversee end-to-end software lifecycle, while ensuring performance and reliability across systems.

Lead design and deliver scalable XVA risk software, embed AI/ML and Generative AI into workflows, own projects end-to-end, drive architecture and Agile delivery, ensure regulatory compliance, and mentor engineering teams.

Drive technology-partner alliances and revenue growth across EMEA. Responsibilities include designing joint go-to-market motions, co-building pipeline through co-sell and marketplace programs, coordinating DevRev and partner sellers on active deals, leading account mapping and joint planning, representing DevRev in partner forums, localizing regional GTM strategy, and reporting on partner-sourced and partner-influenced pipeline.

Own a platform domain end to end, defining architecture, roadmap, reliability, security, and cost standards. Build scalable multi-tenant Kubernetes infrastructure, enable self-service deployments, advance agent-native engineering practices, and develop tooling for safe agent execution. Partner with engineering teams to translate infrastructure needs into reusable capabilities, mentor engineers, lead design decisions, and manage on-call and post-incident processes.

Build and operate the company’s platform infrastructure, including Kubernetes, infrastructure as code, CI/CD, identity, secrets, networking, observability, and developer tooling. Responsibilities include operating production services, supporting agent infrastructure, improving alerting, participating in incident response, and creating self-service capabilities. The role requires strong Kubernetes and cloud experience, Terraform, Helm, GitOps, Python, AI coding-agent expertise, sound engineering judgment, written communication, and comfort with ambiguity.

Own early-stage business development by executing high-volume multichannel outreach, generating and qualifying meetings, nurturing leads, and supporting pipeline growth. Lead initial prospect and partner conversations, deliver software demonstrations, generate demand through AWS and Google Cloud marketplaces, and support events, content campaigns, and other growth initiatives. The role requires energy-sector knowledge, cloud ecosystem awareness, strong communication skills, and independent commercial execution.

Lead architecture and full-stack development of AI-powered platforms, build scalable frontend and backend systems, mentor engineers, translate client problems into production-ready solutions, and ensure high code quality and reliable deployments.

As a Machine Learning Engineer, you will develop software solutions using machine learning, collaborate with clients, and deliver impactful projects working within cross-functional teams.

Design, build and scale full-stack AI-powered systems. Own frontend and backend architectures, lead cross-functional teams, mentor junior engineers, provide technical authority, and collaborate with clients on architecture and deployment.

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.

Build and ship backend-leaning fullstack features (APIs, services, integrations, React frontend). Scope solutions, implement, and own features end-to-end. Collaborate with product and design, contribute to architecture and technical discovery, ensure scalability, reliability, and security best practices, and improve developer experience and tooling.

Lead global Identity & Fraud engineering teams to drive delivery, modernisation, and AI-native practices. Own people leadership, talent strategy, budgeting, vendor management, and cross-functional partnerships. Embed DevOps, cloud (AWS/EKS), Java/.NET full-stack expertise, and AI tools to deliver scalable fraud and identity products for global B2B clients while ensuring governance, security, and operational excellence.

Lead a small team to develop and maintain cloud-based services using JavaScript, TypeScript, and Node. Oversee project delivery, collaborate with cross-functional teams, ensure data security, and optimize performance.

Build and maintain the Enterprise Data Lakehouse on Azure using Databricks, Python and Terraform. Design and implement ETL/data pipelines, automate processes and CI/CD, optimise data infrastructure and pipelines for reporting, ensure data security/compliance, and maintain documentation.

Develop and own scalable backend software supporting genomic research, clinical decision-making, and healthcare innovation. Responsibilities include building Python microservices and REST APIs on AWS, working with relational databases and identity platforms such as Okta, developing automated tests, troubleshooting issues, collaborating with engineering and QA teams, and leading feature development. The role also involves technical collaboration with suppliers and mentoring junior engineers.

Design and lead enterprise GenAI, conversational AI, and agentic AI architectures for Anaplan’s planning platform. Define architecture patterns, APIs, data models, prompt engineering frameworks, observability, evaluation, guardrails, and scalable production systems. Collaborate with product, engineering, and data science teams; evaluate AI technologies; lead architecture reviews; and mentor engineers delivering secure, reliable AI solutions for forecasting, scenario planning, and financial modeling.

Design and implement scalable full-stack applications, owning backend and frontend code across the technology stack. Lead project scoping, solution design, technical requirements, delivery strategy, and implementation of complex initiatives. Build secure APIs and backend services, deploy applications to cloud platforms, resolve roadblocks, improve team processes, and provide technical guidance to less experienced or nontechnical colleagues. The role emphasizes product thinking, autonomy, collaboration, performance, security, and customer value.

Lead a cross-functional Financial Services engineering squad building reliable distributed systems for DeFi, treasury management, and security and compliance. Own delivery, technical quality, service reliability, roadmap execution, hiring, coaching, performance management, and team health. Partner with Product and other teams to establish operating rhythms, OKRs, SLOs, error budgets, on-call practices, and pragmatic trade-offs across roadmap work, technical debt, and operations.

Lead pre-sales data engineering engagements for enterprise customers, including discovery workshops, architecture reviews, executive briefings, migrations, and AI-ready data strategy. Design and advise on Snowflake, Databricks, and lakehouse platforms, translating complex technical tradeoffs into business outcomes. Support services sales opportunities, develop technical thought leadership, engage technology partners, and guide customers on scalable, governed data foundations for AI workloads.

Design, automate, secure, and operate AWS and Azure cloud infrastructure supporting AI and generative AI platforms. Build Terraform-based infrastructure, manage networking, identity, containers, Kubernetes, monitoring, governance, and cost controls. Support production operations, incident response, troubleshooting, documentation, and compliance in partnership with AI, Data, Security, and Application teams.

Lead the design and delivery of scalable, secure enterprise software using Go, distributed systems, Domain-Driven Design, event-driven architecture, and cloud-native technologies. Build production AI-native applications incorporating LLMs, RAG, tool calling, and agentic workflows. Partner with Product and Design, solve complex customer problems, mentor engineers, lead technical initiatives, and promote engineering excellence, observability, testing, security, and operational readiness.