Top AWS Jobs in London

Own the reliability, capacity, cost efficiency, and lifecycle of DeepL’s hybrid CPU and GPU infrastructure across AWS and on-premises environments. Design and operate production Kubernetes clusters, establish infrastructure-as-code and platform standards, improve observability and security, lead incident response, and drive root-cause remediation. The role also shapes platform architecture, advances hybrid cloud integration, mentors engineers, builds cross-team consensus, and supports sustainable on-call operations.

Designs end-to-end data platforms for clients, covering ingestion, orchestration, governance, security, cost optimization, and observability. Leads complex architecture engagements, translates business needs into scalable data solutions, guides engineering teams, supports bids and business growth, and provides technical leadership and mentorship. The role requires strong client-facing communication, data architecture expertise, cloud platform knowledge, and awareness of AI’s impact on data solutions.

Apply population and comparative genomics to non-model organisms: build and run pipelines (variant calling, selection scans, phylogenomics), analyze organellar variation, assemble and annotate genomes and pan-genomes, integrate multi-omic data, apply machine learning where useful, collaborate with wet-lab teams, write reproducible code, present results, and mentor teammates.

Leads Version 1’s AI Centre of Excellence and scales agentic workflows from pilots into core delivery. Owns internal AI tooling, governance, transformation roadmaps, team growth, budget, and impact measurement. Drives AI-native change through executive influence, develops workforce skills and career pathways with People teams, and represents the company’s transformation externally through customer advisory, conferences, press, and thought leadership.

Leads a global data engineering team and drives delivery of the data roadmap. The role combines people management with hands-on work integrating local systems into global data solutions. Responsibilities include coaching engineers, ensuring high-quality delivery, evolving data architecture, and collaborating with engineers and stakeholders. Required expertise includes AWS services, data warehousing platforms, ETL tools, Python, and data modeling.

Lead high-severity security incident response and investigations across cloud environments. Own the incident lifecycle from detection through recovery, improve detection and response capabilities, develop automation and AI-assisted workflows, conduct root cause analysis, maintain operational documentation, communicate with executives, and mentor security engineers within a global 24/7 on-call operation.

Build and deploy production-grade machine learning software, tools, and infrastructure for diverse clients. Develop scalable reusable ML solutions, collaborate with engineers and data scientists, lead technical scoping and architecture decisions, establish deployment standards, and advise technical and non-technical stakeholders. The role requires expertise across the ML lifecycle, Python software engineering, cloud infrastructure, containerization, orchestration, and core machine learning concepts.

Principal Engineer responsible for building and operating production software at scale across multiple codebases. The role involves shipping end-user products, improving event and data pipelines, diagnosing performance bottlenecks, influencing technical direction, establishing agentic engineering practices, and collaborating with data scientists. This hands-on individual contributor role requires broad experience across Python, Node.js, React, Rust, data science, cloud infrastructure, security, privacy, and regulated financial software.

Leads technology and cyber risk management for financial markets platforms. Responsibilities include conducting technical risk deep dives, assessing and monitoring risks, designing and testing controls, reviewing architectures, validating evidence from logs and configurations, identifying attack paths through threat modeling, documenting deficiencies, and advising stakeholders on mitigation strategies and regulatory compliance.

Own and evolve AWS-based production and development infrastructure for high-volume, low-latency sports trading systems. Responsibilities include automation, configuration management, networking, security, monitoring, incident diagnosis, performance troubleshooting, database administration, and infrastructure repeatability. The engineer will work closely with development leadership, analyze metrics, packet captures, and logs, and participate in a shared compensated out-of-hours on-call system.

Set technical strategy and architecture for enterprise CI/CD, release engineering, and internal developer platforms. Lead deployment automation, infrastructure tooling, SRE practices, DORA metrics, developer productivity, and AI-enabled workflows across engineering teams. Establish global DevOps standards, govern secure and scalable AWS, Kubernetes, Terraform, GitHub Actions, and ArgoCD environments, and mentor senior engineers while contributing hands-on to critical release infrastructure.

Designs end-to-end data architectures and platforms for complex client programs, covering ingestion, orchestration, governance, security, cost optimization, and observability. Leads engineering teams, translates business needs into scalable solutions, advises clients on data and AI transformations, and provides strategic technical leadership. Responsibilities also include assignment delivery, team development, thought leadership, bid leadership, and business growth in a consulting environment.

Develop and maintain production data and machine learning platforms, APIs, pipelines, and services supporting live retail planning systems. Package, serve, version, and monitor machine learning models; improve CI/CD and deployment processes; define architectures and service boundaries; collaborate with data scientists and engineering teams; and provide on-call support for mission-critical tools.

Designs and develops backend services for Trainline’s insurance products using C# and .NET. The role includes making technical decisions, mentoring engineers, owning software from development through deployment and monitoring, improving code quality, and removing delivery blockers. Responsibilities involve building scalable microservices and event-driven systems, working with AWS and infrastructure tooling, maintaining observability and reliability, and collaborating across engineering and product disciplines.

Drive net-new enterprise sales of Databricks’ Data and AI platform across UKI public sector organizations. Build territories, self-source pipeline, navigate public sector procurement, engage executive and economic buyers, lead complex sales cycles, and close new-logo business. Partner with cloud providers, solution architects, services, and other cross-functional teams to demonstrate business value, support consumption-based adoption, and achieve quota. The role requires strong enterprise selling, account planning, forecasting, AI tool usage, and eligibility for SC clearance.

Entry-level AI engineering role providing hands-on experience with data preprocessing, exploratory analysis, machine learning model development, testing, optimization, and deployment. The role includes collaboration with AI researchers and engineers, documentation, and exposure to neural networks, natural language processing, and computer vision. Mentorship and flexible scheduling support candidates beginning careers in artificial intelligence.

Designs, builds, and operates scalable machine learning platforms supporting training, deployment, inference, search, recommendations, and personalization. Responsibilities include cloud-native infrastructure, backend services, CI/CD, observability, automation, distributed computing, Infrastructure as Code, incident management, and architectural leadership. The role collaborates with data scientists, engineers, and product managers, owns production reliability, mentors engineers, and shapes long-term MLOps and AI platform strategy.

Own and improve AWS cloud infrastructure, Terraform-based infrastructure as code, GitHub Actions CI/CD, observability, and production reliability. Responsibilities include on-call operations, incident response, failover planning, security and compliance controls, secrets management, automation, self-service tooling, and runbooks. The role partners with engineering, Security, and GRC to support resilient, auditable infrastructure for a regulated clearing platform.

Design and maintain scalable data platforms, lakehouse architectures, pipelines, and data products across cloud and on-premises environments. Build streaming, ETL/ELT, transformation, orchestration, CI/CD, governance, and self-service analytics solutions. Collaborate with stakeholders and architects to translate business needs into technical solutions, support data quality and compliance, and provide operational support. Explore generative AI tools such as Amazon Q and Copilot to improve analytics accessibility and efficiency.

Build and operate AI backend systems, agentic workflows, RAG pipelines, evaluations, and LLM-powered data pipelines. Develop data-layer capabilities, monitoring, inspection interfaces, and production-grade software across Python, Dagster, PostgreSQL, Elasticsearch, and Iceberg. Collaborate with product, research, and commercial teams, own technical projects and PoCs, and balance security, quality, and delivery speed in a high-ownership startup environment.

Leads company-wide cloud infrastructure strategy, architecture, operations, reliability, security, observability, disaster recovery, and cost optimization. Drives Infrastructure-as-Code, cloud-native transformation, automation, self-service capabilities, and AI infrastructure strategy. Builds and manages an infrastructure team while partnering with engineering, security, product, and performance teams to improve deployment velocity, system reliability, compliance, and cloud efficiency.

Lead and develop a high-performing engineering team while remaining hands-on with code and technical strategy. Own engineering delivery, architecture, hiring, team development, and collaboration with Product on roadmap priorities. The role requires scaling enterprise systems, solving distributed-systems challenges, operating in a high-growth startup, and shaping data and ML capabilities. The manager will work across the AWS, NodeJS, TypeScript, Postgres, ClickHouse, Kubernetes, and NextJS stack.

Design, build, test, deploy, and support low-latency Java services for an order management and trading platform. Responsibilities include market connectivity using FIX, order lifecycle modeling, distributed systems, data integrity, observability, production support, incident response, mentoring, and AI-assisted development. The role collaborates closely with traders, dealers, quants, and business stakeholders while participating in on-call rotations.

Own platform operations, BAU support, QA coordination, release readiness, service transition, documentation, training, stakeholder communication, and post-release improvement. The role ensures solutions are tested, documented, supported, adopted, and operationally ready before launch. It coordinates UAT, defect tracking, business validation, runbooks, support processes, operational governance, and risk escalation across product, engineering, data, QA, and business teams.

Leads global enterprise partnership, product, and funding strategies across the Google, Meta, and Amazon ecosystems. Builds senior partner relationships, secures investment, develops business cases and statements of work, and translates partner technologies into scalable product propositions, integrations, and delivery roadmaps. Oversees funded commitments, commercial outcomes, adoption, delivery risks, and cross-functional alignment across product, commercial, finance, legal, clients, and global markets.