Top AWS Jobs in London

Design and deploy production-scale AI systems, develop and optimize machine learning and neural network models, work with large language models, refine prompt engineering techniques, and use OpenAI and Anthropic APIs. Collaborate with interdisciplinary teams to build AI applications for the finance sector while keeping current with industry developments.

Build and enable Chainguard’s partner ecosystem through technical training, workshops, labs, reference architectures, demos, integrations, and joint solutions. Collaborate with cloud providers, VARs, systems integrators, technology partners, Sales, Product, and Engineering teams to drive partner-led adoption and revenue. Engage technical and executive stakeholders, support cloud-native and secure software architectures, and maintain ongoing partner relationships. The role requires cloud, containers, Kubernetes, DevSecOps, and partner enablement expertise, with travel up to 50%.

Build high-fidelity prototypes and interactive experiences across web, mobile, and TV platforms. Develop internal tools, Figma plugins, shared frameworks, APIs, and AI-powered utilities that improve design workflows. Collaborate with designers, engineers, product managers, and researchers, integrate live or simulated data, iterate based on feedback, and mentor other design engineers. The role emphasizes front-end engineering, interaction design, rapid experimentation, emerging technologies, and streaming media experiences.

Lead the technical direction of data engineering across client engagements. Design migration frameworks, streaming architectures, Lakehouse platforms, data integrity systems, and regulatory reporting pipelines. Provide architectural authority, review technical designs, select cloud services, mentor senior engineers, develop reusable delivery patterns, support hiring, and represent the consultancy externally. Shape proposals, scope technical engagements, present to senior stakeholders, and identify opportunities with existing clients.

Lead cloud engineering delivery for client and internal projects, providing technical direction, hands-on infrastructure development, platform engineering, AI enablement, data engineering, security, governance, and FinOps. Build infrastructure as code, CI/CD pipelines, containerized applications, Kubernetes platforms, AI agents, data orchestration systems, observability, and cost controls. Advise clients, contribute to pre-sales, establish engineering standards, mentor cloud engineers, support recruitment, and grow the cloud capability.

Own production data and feature pipelines supporting machine learning systems, including training data preparation, feature engineering, model deployment, versioning, retraining, monitoring, and CI/CD. Build batch and ELT pipelines using Python, PySpark, and cloud data platforms. Troubleshoot live systems, make architecture decisions, partner with clients and data scientists, and mentor engineers while delivering solutions with substantial autonomy.

Lead hands-on backend engineering using Java, Spring Boot, REST APIs, and Kafka-based services. Own technical direction, delivery, architecture, testing, integrations, and DevOps practices while mentoring engineers and championing AI-assisted development. Manage workstream risks, timelines, bottlenecks, client communication, estimation, and delivery planning in a fast-paced consultancy environment.

Leads complex client software engagements through hands-on development, architecture, solution design, delivery management, and technical decision-making. Manages and mentors engineers, establishes engineering standards, troubleshoots complex issues, and oversees scope, estimation, planning, risks, dependencies, and outcomes. Builds trusted client relationships, translates business needs into technical plans, manages expectations, and identifies follow-on opportunities. The role also contributes to recruitment, pre-sales, consultancy practices, and technology thought leadership.

Build reliable, well-tested platform software for AI-powered industrial control systems. Responsibilities include developing with Python and Terraform, supporting production systems, improving architecture, mentoring engineers, strengthening software practices across engineering and ML teams, and enhancing system quality, maintainability, and reliability. The role spans CI/CD, networking, plant-system data integration, IoT operating systems, and customer-facing control software.

Lead reliability and operational improvement initiatives for GoDaddy’s Commerce platform. Own critical production systems, incident response, post-incident improvements, cloud infrastructure, automation, observability, and deployment platforms. Partner across engineering, infrastructure, security, networking, and product teams to improve scalability and resilience. Mentor engineers, influence technical direction, reduce operational toil, and use data and AI-assisted tools to improve troubleshooting and delivery.

Develop and maintain database code, including stored procedures, functions, and schema changes. Investigate production data incidents, apply controlled fixes, optimize queries, and improve database performance. Build automated data checks, monitoring, and alerts; test application compatibility during database upgrades; and support data migration and modernization efforts. The role also contributes to database standards, documentation, CI/CD practices, and AI-assisted development.

Build scalable backend systems with Go and responsive frontends with React and TypeScript. Lead architecture and technical design, develop secure APIs, drive testing and engineering excellence, mentor engineers, and deliver client projects. The role also involves evaluating emerging technologies, implementing AI-enabled engineering practices, automating workflows, and collaborating with technical and non-technical stakeholders across discovery and delivery.

Own the architectural roadmap for AlphaSense’s cloud, Kubernetes, storage, and configuration platforms. Lead cell-based infrastructure scaling, automate provisioning, standardize cross-cell controls, and operate hundreds of clusters. Improve security, reliability, cost, and performance while developing self-service, AI-enabled tooling. The role requires deep AWS, Kubernetes, storage, networking, operational, and software development expertise, plus the ability to influence senior engineering teams and deliver ambitious technical programs.

Leads and develops a team of 11 technical cloud solution architects across the UK and Ireland. Drives Azure adoption, consumption, pipeline generation, revenue growth, forecasting, territory planning, and strategic account execution. Partners with sales, partner, and executive stakeholders to accelerate customer digital transformations, resolve technical blockers, and deliver business outcomes. Owns the regional business rhythm, coaching teams to exceed performance targets while maintaining forecast accuracy and operational rigor.

Own and operate globally distributed GPU infrastructure for model training, evaluation, and serving. Responsibilities include running Kubernetes GPU clusters, optimizing scheduling and placement, repairing unhealthy nodes, managing cloud capacity, and debugging Linux, hardware, storage, and networking failures. The role also involves designing GPU clusters, collaborating with research and product teams, and improving compute reliability and utilization.

Own and operate GPU infrastructure for model training, evaluation, and serving. Responsibilities include running Kubernetes GPU clusters, improving scheduling and placement, repairing unhealthy nodes, managing cloud capacity, and debugging Linux, GPU, storage, and networking failures. The role partners with infrastructure, research, product, and cloud-provider teams to build globally distributed compute systems.

Develop and productionize machine learning models for audience targeting, advertising measurement, and content intelligence. Build scalable batch and real-time serving infrastructure, feature pipelines, APIs, monitoring, CI/CD, and reproducible environments. Establish MLOps engineering standards, reusable templates, and documentation while auditing and migrating existing production models. Partner with data science, MLOps, data engineering, and product teams to deliver reliable, maintainable ML systems.

Build, deploy, and scale production machine learning and deep learning solutions for digital advertising, including audience identity, targeting, and attribution. Develop real-time inference systems, scalable ML pipelines, monitoring, drift detection, and automated retraining on AWS. Collaborate with data engineering and product teams using Spark and Databricks, while mentoring technical professionals and improving model reliability, performance, and cost efficiency.

Build and maintain Turso’s open-source database and cloud platform. Responsibilities include advancing PostgreSQL compatibility, ensuring software correctness through testing and verification, operating cloud APIs and data planes, and scaling systems to support substantially more databases. The role involves Rust development, embedded database technology, large-scale cloud operations, observability, reliability, performance improvements, and collaboration with customers, open-source communities, and distributed teams.

Leads high-stakes digital forensics and incident response investigations, including nation-state attacks, ransomware, and edge-device compromises. Directs host, network, and cloud forensics while preserving court-defensible evidence. Advises enterprise executives, leads cyber resilience assessments and tabletop exercises, manages customer relationships, supports pre-sales and RFPs, delivers thought leadership, and mentors consultants across EMEA.

Build and deliver production-grade AI and GenAI solutions for enterprise clients, including agentic systems, RAG pipelines, automation workflows, and model orchestration layers. Lead AI transformation engagements from strategy and architecture through deployment and scaling. Support presales, thought leadership, proposition development, mentoring, and technical capability building while advising clients on AI architecture, evaluation, cost, latency, scalability, and responsible AI.

Supports enterprise sales by discovering customer requirements, mapping them to SailPoint solutions, and delivering foundational implementations and proof-of-value engagements. Acts as a technical lead and consultative advisor, educating customers on architecture and best practices. Collaborates with sales, product, professional services, partners, and customer success teams while developing AI-driven and agentic solutions for demonstrations and customer outcomes.

Manage company-wide risk assessments, risk registers, remediation tracking, control testing, audits, evidence collection, and SOC 2 and ISO 27001 compliance operations. Partner with engineering, security, IT, and legal to validate controls, automate evidence gathering, investigate failures, support customer and vendor security reviews, and improve continuous compliance practices in a fully remote environment.

Design and deliver secure, scalable, resilient AI and Generative AI architectures for enterprise use. Translate business challenges into production-ready solutions using LLMs, RAG, embeddings, vector search, AI agents, orchestration, and enterprise integration. Provide hands-on architectural guidance across Engineering, Data, Product, and Security; evaluate technologies and trade-offs; establish reusable AI patterns and standards; and promote governance, Responsible AI, operational resilience, and maintainability.

Build, scale, and operate Databricks and AWS infrastructure supporting automated, reproducible machine learning workflows. The role includes infrastructure-as-code with Terraform or Pulumi, containerized platforms using Docker and Kubernetes, system architecture, reliability and cost optimization, and cross-functional collaboration with data and ML teams. The engineer will lead complex projects, improve platform scalability and usability, document systems, and mentor peers while operating production-scale data and ML platforms.