Top AWS Jobs in London

Serve as the technical lead for a portfolio of customers, designing and delivering SaaS integrations, driving adoption and measurable business outcomes, building reusable AI-first delivery artefacts, managing delivery scope and risks, and sharing best-practice patterns across the team.

Lead data engineering and platform teams in designing, delivering, and modernizing scalable enterprise data platforms. Make architectural decisions across ingestion, storage, processing, modeling, orchestration, governance, security, and serving. Establish software engineering, testing, CI/CD, infrastructure-as-code, observability, and data quality practices. Mentor engineers, guide clients through complex transformations, contribute to bids and proposals, and help teams apply GenAI with appropriate controls.

Partners with Account Executives to support enterprise software sales by understanding customer needs, designing solution architectures, delivering customized demonstrations and proofs of concept, answering technical questions, and managing RFPs/RFIs. Builds relationships with technical stakeholders, supports events, monitors data and AI trends, and provides product feedback to engineering and product teams.

Owns client incidents and technical requests throughout their lifecycle. The role triages complex application, infrastructure, and business-flow issues; advises clients on installation, configuration, deployment, connectivity, and product use; documents resolutions; escalates unresolved issues to engineering teams; and participates in a global 24x7x365 on-call rotation. Requires hands-on cloud, containerization, databases, networking, operating systems, blockchain, and application troubleshooting experience.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Design, build, and operate Supabase’s global edge and networking infrastructure, including routing, load balancing, DNS, WAF, TLS termination, CDN integrations, and observability. Improve latency, reliability, security, and cost efficiency across cloud regions. The role includes developing automated routing systems and CI/CD, troubleshooting distributed systems, participating in on-call and incident response, and collaborating with infrastructure and product teams.

Build and mature security engineering capabilities for cloud-native, regulated trading systems. Design security controls, AI security guardrails, CI/CD and infrastructure-as-code checks, monitoring, incident response, resilience processes, and compliance documentation. Conduct risk assessments, remediate findings, support regulatory audits, and partner with engineering teams to embed security ownership. The role requires hands-on cloud, container, application security, automation, and DevSecOps expertise, with familiarity with CFTC safeguards expectations.

Build and maintain full-stack web applications and automations for Central Operations using Python, FastAPI, React, and Next.js. Deliver tools used by operations teams, improve workflows through intelligent automation, and collaborate with Product, Data Science, and Analytics. Contribute to technical direction, apply strong engineering practices, and explore generative AI and LLM solutions to improve customer support, fraud prevention, complaints processing, and operational efficiency.

Build and ship AI-powered product capabilities for enterprise IT operations. Design production systems involving LLMs, agents, RAG, vector search, knowledge graphs, workflow orchestration, APIs, integrations, and automation. Own features from customer discovery through implementation, launch, troubleshooting, and iteration. Collaborate directly with customers and cross-functional teams while making pragmatic tradeoffs around reliability, cost, latency, and scalability.

Lead the development of AI-powered personalized learning solutions, including deep learning, NLP, information retrieval, LLM, and multimodal systems. Shape Preply’s scientific roadmap, advise senior leadership, partner with product and engineering teams, mentor scientists and engineers, and establish machine learning architecture, research, and deployment best practices. The role spans research through production and focuses on scalable solutions for personalized learning and intelligent tutor systems.

Design, implement, and maintain AWS cloud infrastructure, deployment architectures, CI/CD pipelines, high-availability systems, monitoring frameworks, and Kubernetes-based applications. Automate infrastructure with Terraform or CloudFormation, apply security best practices, optimize performance and scalability, and collaborate with development teams and clients to improve deployment workflows and deliver projects effectively.

Build and maintain internally facing web applications and automations for Operations teams. Develop full-stack features using Python, FastAPI, JavaScript or TypeScript, React, and Next.js. Collaborate with Product, Data Science, and Analytics teams to deliver scalable operational tools and pragmatic AI solutions, including LLM-powered workflows. Take ownership of technical decisions, testing, code quality, system design, and feature delivery in a product-focused environment.

Build and improve Lendable’s data platform by delivering scalable, reliable, secure, and cost-effective data solutions. Partner with product, analytics, and engineering teams to enable fast analytics and insight generation. Responsibilities include developing data-intensive systems, applying modern data architectures, managing cloud infrastructure and infrastructure-as-code, supporting CI/CD, evaluating build-versus-buy decisions, and improving platform operations while working autonomously in an ambiguous environment.

Build and own production-grade agentic AI systems end to end, including multi-agent orchestration, LLM and vision-language model integration, retrieval, memory, guardrails, evaluation, observability, and deployment pipelines. The role requires designing reliable systems for cloud, on-premises, air-gapped, and offline environments; handling large multimodal datasets; and setting technical direction for other engineers. Strong Python, software engineering, production AI/ML, and secure deployment experience are required.

Lead and develop a product engineering squad, balancing people leadership, delivery accountability, and technical guidance. Responsibilities include recruiting, coaching, planning, delivery health, production readiness, system reliability, security, scalability, and maintainability. Partner with product managers and engineering leaders to align priorities, guide technical decisions, improve delivery predictability, and support responsible use of AI-assisted engineering tools.

Leads end-to-end performance testing and assurance for complex digital services. Owns performance strategy, risk assessment, test scoping, success criteria, load, stress, soak, and scalability testing. Provides architectural recommendations, integrates testing into CI/CD pipelines, analyzes results, leads root-cause investigations, and signs off completion and production-readiness reports. Engages senior stakeholders and governance forums, supports operational acceptance testing, mentors testers, and drives continuous improvement.

Lead the design, implementation, maintenance, scalability, reliability, and security of Kubernetes infrastructure and cloud platforms. Provide technical leadership, mentorship, people management, and guidance on cloud-native engineering, Agile delivery, security, compliance, disaster recovery, high availability, and cost optimization. Manage the Kubernetes container team backlog, drive continuous improvement, collaborate with cross-functional teams, and establish software engineering and infrastructure best practices.

Lead client implementations of Seqera Platform across cloud, hybrid, and on-premises environments. Deploy infrastructure, networking, security, storage, containers, and compute resources; build reusable infrastructure-as-code tooling, tests, and documentation; and guide clients through technical constraints. Support pre-sales and proof-of-concept deployments, serve as a senior escalation point, and turn recurring implementation issues into lasting solutions.

Leads client-facing data engineering delivery using Databricks and cloud platforms. Develops batch and streaming pipelines, designs lakehouse and medallion architectures, models and optimizes data with Delta Lake, and establishes governance, security, quality, and CI/CD practices. Collaborates with architects, engineers, analysts, and executive stakeholders while mentoring engineering teams and contributing reusable technical assets and best practices.

Lead end-to-end technology procurement for cloud, SaaS and IT services. Manage high-value sourcing, supplier negotiations (e.g., AWS, Microsoft), contract lifecycle, supplier performance and risk. Develop category strategies, identify cost optimisation, support stakeholder decision-making and assist supplier transitions during operational change.

Provides technical sales support across customer and partner accounts by conducting discovery, demonstrations, proof-of-concepts, solution design, architecture documentation, integrations, and RFP responses. Acts as a trusted technical advisor, translating customer requirements into tailored solutions involving PKI, cloud platforms, APIs, automation, and cybersecurity technologies. Collaborates with account executives, customer success, product, and engineering teams throughout the sales cycle. Travel is expected 20–25%.

Lead end-to-end technical strategy for customer accounts, from discovery through deployment and platform adoption. Design scalable solutions across data engineering, machine learning, AI, streaming, real-time analytics, and lakehouse architectures. Advise senior technical stakeholders, lead architecture discussions, build demonstrations, drive competitive technical wins, coordinate internal and partner resources, and provide product feedback. Develop a technical specialization and communicate complex architectures as business value.

Lead end-to-end technical strategy for customer accounts, from discovery through deployment and platform adoption. Design scalable solutions across data engineering, machine learning, AI, streaming, and real-time analytics. Advise senior technical stakeholders, lead architecture discussions, demonstrate competitive differentiation, coordinate cross-functional resources, develop technical specialization, and influence product direction through customer feedback.

Owns the roadmap and delivery of enterprise AI platform capabilities, including LLM and MCP gateways, agent runtimes, access controls, audit logging, governance, cost attribution, and cloud AI integrations. Partners with engineering, SRE, security, data, and business technology teams to define scalable architecture, APIs, self-service builder experiences, and prioritized product requirements. Leads products from problem definition through launch, adoption, and iteration while evaluating enterprise AI market trends and build-versus-buy decisions.

Lead the design and delivery of AI-based solutions for enterprise customers, ensuring successful integration and optimization across multiple projects, while mentoring junior engineers.