Top AWS Jobs in London

Designs, implements, tests, and maintains automated and AI-driven security detections and integrations across SIEM, EDR, SOAR, and threat intelligence platforms. Analyzes security telemetry, improves detection coverage, develops automation workflows, and documents SOPs and runbooks. Collaborates with SOC, threat intelligence, and incident response teams while evaluating emerging security and AI/ML capabilities. Requires substantial cybersecurity experience, relevant certifications, scripting proficiency, and U.S. citizenship or permanent residency.

Design and deliver production-grade GenAI and applied machine learning solutions, including LLM/RAG systems, model endpoints, evaluation, observability, governance, and safety controls. Collaborate with data, engineering, product, and business teams across cloud platforms. Assess data readiness, communicate trade-offs, lead workstreams, mentor colleagues, and contribute to consulting pursuits and reusable AI assets.

Build and own Zoopla’s production data platform, including standardized ingestion and export patterns, pipeline migration, governance, data quality, access controls, lineage, and cost management. The role involves Databricks and AWS development, infrastructure as code, AI-enabled engineering, code review, mentoring, and production support. It requires simplifying complex pipeline ecosystems while delivering business impact and collaborating with platform, analytics, and data science teams.

Lead real-world evidence biostatistical analyses using large-scale healthcare data to support clinical development, regulatory strategy, HEOR, and payer decisions. Design observational studies and target trial emulations, apply causal inference, statistical learning, machine learning, and NLP, and build reproducible Python/R workflows. Collaborate with clinical, epidemiology, data engineering, and HEOR teams while translating findings for technical and non-technical stakeholders. Support regulatory submissions, governance, audits, and scientific dissemination.

Lead day-to-day product management for the Lens data analytics platform across commodities sectors. Define user requirements and acceptance criteria, coordinate Research, Data, UX, and Engineering teams, support documentation and testing, communicate priorities and risks, gather customer feedback, and collaborate with Sales and Marketing on go-to-market initiatives.

Lead Zoopla’s four-person Data Engineering team, set technical direction, establish scalable ingestion and export patterns, and build a shared roadmap with platform and product leaders. Own data governance, AI adoption, Fivetran costs and vendor relationships, while coaching engineers and partnering on lakehouse architecture. The role requires expertise in Databricks, dbt, AWS, Terraform, ingestion patterns, data quality, access control, and personal data handling. It is hybrid, with three office days weekly in London.

Own production machine learning operations, including model deployment, monitoring, retraining, CI/CD, versioning, and reusable MLOps standards. Build secure infrastructure for AI tools and agents to access company data, maintain GCP and AWS platforms, and monitor reliability, costs, drift, and data access controls. Collaborate with data scientists and engineers to deliver maintainable, secure, observable ML and AI systems.

Lead application modernisation architecture across pre-sales and delivery engagements. Design target architectures, migration strategies, and incremental approaches for legacy systems while maintaining business continuity. Conduct client workshops, explain technical solutions to non-technical stakeholders, scope work, estimate proposals, and support bids. Deliver hands-on architecture, decomposition, migration, and re-platforming work; advise on AI tooling; establish engineering standards; mentor engineers; and transfer knowledge to client teams.

Serve as the technical authority for strategic enterprise sales, leading requirements workshops, solution architecture, product demonstrations, technical blueprints, integrations, and proofs of concept. Design content intelligence workflows connecting TMS, CMS, DMS, and enterprise platforms, including AI and human review processes. Partner with Sales, Product, Engineering, Delivery, and Operations to validate solutions, address security and scalability needs, automate QA, and ensure successful implementation and handoff.

Leads a team of Data Scientists and delivers machine learning and statistical modelling solutions across the insurance business. Owns projects from exploration and experimentation through production deployment, monitoring and optimisation. Partners with stakeholders, Data Engineering and Technology teams to identify commercial opportunities, build scalable models, evaluate data sources, ensure governance, and translate technical findings into actionable business insights.

Leads a team of Data Scientists and shapes data science strategy across the insurance business. Develops and deploys machine learning, statistical, and predictive models across the full lifecycle, including experimentation, production deployment, monitoring, and optimization. Partners with engineering, technology, and senior stakeholders to identify commercial opportunities, evaluate data sources, govern models, and translate technical outputs into actionable business insights.

Manage and mentor a team of software engineers while contributing hands-on to software design, development, deployment, maintenance, code reviews, troubleshooting, and technical decisions. Lead delivery and engineering quality, collaborate with product and design stakeholders, improve team processes, and implement CI/CD pipelines, automated testing, observability, and secure coding practices.

Develop and grow a defined sales territory by prospecting, building pipeline, acquiring net-new customers, expanding existing accounts, and closing enterprise technology opportunities. The role involves engaging cybersecurity stakeholders, presenting value-based proposals, collaborating with channel partners and technology alliances, forecasting opportunities, and sharing customer insights. Solutions focus on data security, backup, ransomware recovery, and AI-powered resilience.

Leads Azure and artificial intelligence sales strategy for complex global enterprise accounts. Engages senior executives, identifies opportunities, aligns cloud solutions with business priorities, orchestrates internal and partner teams, manages pipeline forecasting, and drives secure digital transformation and customer adoption across industrial manufacturing and communications organizations.

Own technical customer relationships throughout the lifecycle, from discovery and proof of concept through onboarding, deployment, adoption, and expansion. Partner with sales on solution design, demonstrations, technical discovery, upselling, and customer acquisition. Advise technical stakeholders, support cloud and network architectures, coordinate onboarding and troubleshooting, create enablement materials, communicate customer needs to product teams, and provide competitive technical insights.

Build and support customer-focused software as a senior product engineer in a cross-functional team. Develop modern web applications, contribute across frontend and backend systems, operate production services, and improve observability, testing, and engineering practices. Collaborate with customers and product partners while working across AWS, GCP, and Azure using agile, event-driven, containerized, and serverless architectures.

The Principal Solutions Architect leads technical solution design, partners with hyperscalers, ensures architectural integrity, and drives customer engagement through compelling narratives and demos.

Lead hypothesis-driven threat hunts across customer environments, correlating endpoint, cloud, identity, network, and third-party telemetry. Investigate adversary activity, determine compromise scope, identify visibility gaps, author queries and automation, and convert findings into detections, mitigations, threat intelligence, and architectural improvements. Coordinate hunt workstreams during major incidents, brief customers and executives, document evidence, mentor colleagues, and support post-incident analysis.

Build software systems, services, and tools supporting investment portfolio management and construction. Develop cloud-first, data-centric applications using modern programming languages and AWS services; apply software engineering best practices, observability, and AI-enabled analytics. Collaborate with engineers, customers, and partner teams, contribute to technical decisions, identify bottlenecks, and solve ambiguous problems while delivering scalable solutions.

Build and operate production infrastructure for reliable agentic AI systems. Responsibilities include durable agent runtimes, secure tool integration, state and memory management, permissions, guardrails, observability, evaluation, SDKs, workflow frameworks, SLOs, and incident response. The role partners across engineering, security, product, data, and applied science while shaping platform architecture and developer experience.

Designs and operates production GenAI platform infrastructure, including model gateways, routing, caching, evaluation, observability, prompt lifecycle management, guardrails, cost controls, and incident response. Partners with engineering, security, privacy, product, data, and applied science teams to create reliable self-service capabilities for AI workloads.

Lead hands-on full-stack and AI engineering for enterprise data applications. Design, build, test, deploy, and operate APIs, data workflows, user interfaces, and production generative AI solutions using retrieval-augmented generation. Establish evaluation, observability, security, governance, and cost controls for AI services. Provide technical leadership, mentor engineers, influence stakeholders, and develop reusable architectures, services, and engineering practices across global teams.

Lead full-stack software and AI engineering for enterprise data applications. Design, build, test, deploy, and operate APIs, user interfaces, data workflows, and generative AI solutions, including retrieval-augmented generation systems. Establish evaluation, observability, security, and operational practices for production AI. Provide technical leadership, mentor engineers, influence stakeholders, and develop reusable architectures and services across global teams.

Lead customer-facing delivery of production-grade data and AI solutions using Databricks. Own architecture and design decisions, build end-to-end data pipelines, applications, and ML/AI integrations, and guide enterprise customers through implementation. Collaborate with engineering, product, project management, and support teams; manage technical scope and outcomes; provide training and documentation; and develop reusable frameworks and best practices. Travel to customers approximately 20% of the time.

Serve as a technical advisor to enterprise customers, architecting, securing, administering, and automating Databricks deployments across AWS, Azure, and GCP. Lead cloud networking, identity, infrastructure-as-code, platform operations, and distributed data architecture discussions. Demonstrate custom solutions in competitive sales engagements, support platform adoption, collaborate with field teams and partners, influence product direction, and contribute to customer-facing thought leadership. The role requires strong Python and PySpark/Spark coding, Databricks expertise, executive communication, and up to 30% travel.