Top AWS Jobs in London

Lead the design, development, and deployment of scalable, production-grade machine learning systems. Establish ML engineering standards, make architectural decisions, collaborate with technical and commercial stakeholders, advise clients, and mentor junior engineers. The role requires expertise across the ML lifecycle, cloud infrastructure, software engineering, containers, orchestration, and responsible AI delivery.

Lead architecture and technical strategy across multiple product teams while building and shipping full-stack features. Mentor engineers, promote AI adoption throughout the software development lifecycle, deliver LLM-backed capabilities, guide system evolution, own release processes, and support reliable continuous deployment. The role works across mobile, backend, integrations, cloud infrastructure, and production support while collaborating with Product, Design, and Engineering leadership.

Lead complex digital forensic and investigative engagements for clients, advising on regulatory and legal matters, conducting device/cloud forensics, directing mobile extractions and analyses, developing cloud forensics methodologies, integrating automation and AI techniques, acting as expert witness, mentoring junior staff, and collaborating across EMEA and global teams. Travel to client sites in the UK and internationally as required.

The Data Scientist will turn messy media and audience data into business insights and decisions. Responsibilities include sourcing and cleaning data, analyzing trends and audience behavior, designing experiments, building and deploying forecasting and content-analysis models on AWS, maintaining trusted dashboards and reports, and applying LLMs and agentic coding tools. The role works autonomously with engineering, product, business, and company leadership in a fast-paced remote startup environment.

Leads a distributed engineering team across hiring, development, performance, delivery, and technical quality. Partners with product management on roadmaps, manages dependencies, drives planning and retrospectives, and oversees reliability, observability, testing, incidents, and operational follow-up. Contributes to engineering-wide standards and process improvements while developing engineers and ensuring the team delivers meaningful, maintainable outcomes.

Graduate consultants analyze datasets, build dashboards and visualizations, support data pipelines and transformations, ensure data quality and governance, and communicate insights to stakeholders. The role involves client-facing, on-site work within transformation programs, supported by structured training, mentoring, and professional accreditations. Opportunities span data analysis, business intelligence, data engineering, governance, risk and compliance, and data science.

Build and maintain production data engineering pipelines using SQL, dbt, and Python. Contribute to data ingestion, transformation, API-serving workflows, testing, debugging, cloud infrastructure, and AI-assisted engineering. Collaborate with data engineers, analysts, and product stakeholders while learning through mentorship, code reviews, and hands-on early-career training.

Lead design and implementation of secure, scalable cloud data architectures; drive adoption of AWS/Azure/GCP services; collaborate with data engineers to deliver data for analytics; assess data management technologies, promote data governance, mentor staff, and articulate architectural trade-offs to improve data quality and delivery supporting business roadmaps.

Leads and scales Databricks’ EMEA Forward Deployed AI Engineering team, overseeing hiring, mentoring, customer relationships, strategic AI professional services engagements, delivery processes, account outcomes, and cross-functional collaboration with Sales, Product, Research, Engineering, and GTM teams. The role guides enterprise customers through AI/ML transformations, develops scalable engagement models, manages executive stakeholders, and drives production ML and GenAI solutions in cloud environments.

Graduate data engineering role focused on building and maintaining SQL/dbt transformation pipelines, Python ingestion services, API data pipelines, and data quality processes. The role includes production coding, testing, debugging, code reviews, cloud infrastructure exposure, AI-assisted engineering, and collaboration with engineers, analysts, and product stakeholders. It is designed for recent graduates with foundational SQL and Python experience and offers mentorship, training, and cross-functional development opportunities.

Build and maintain full-stack features using React or Next.js, Python, databases, and APIs. Write tested code, participate in reviews and design discussions, resolve bugs, improve test coverage, and collaborate with product, design, and engineering teams throughout the software development lifecycle. The role also involves AI-assisted development, production monitoring, and progressively increasing ownership of system areas.

Software Engineering Interns will help design, maintain, and scale infrastructure supporting Talos’s digital asset trading and settlement platform. Responsibilities include contributing to CI/CD, infrastructure-as-code, deployment tooling, monitoring, logging, observability, and production performance improvements. Interns will collaborate with engineering teams to implement infrastructure requirements and gain exposure to distributed systems, cloud platforms, Linux, networking, and platform engineering during a 12-week summer internship.

Lead development and global rollout of a machine learning research framework for training, evaluating, combining, and deploying trading models. Partner with quantitative researchers to test hypotheses, develop systematic trading strategies, and create alpha signals. Build end-to-end machine learning pipelines covering data preprocessing, model training, production deployment, and monitoring while applying modern software engineering, cloud, containerization, and MLOps practices.

Full-stack mid-level engineer building and operating secure research environments (frontend, backend, infrastructure). Work includes Typescript/Node development, IaC (Terraform), Azure/Kubernetes, data modelling, operational management, and collaborating with cross-functional teams to enable secure researcher access to sensitive health data.

Designs and deploys autonomous AI agent ecosystems and LLM-powered workflows using leading orchestration frameworks. The role includes enterprise architecture, agent memory and tool integration, client workshops, pre-sales and RFP support, solution blueprints, cost estimation, reusable accelerators, AI governance, and technical mentorship. Candidates collaborate with consulting and domain teams to deliver client-centric AI solutions across industries.

Designs and operates hybrid-cloud platforms across AWS, Azure, and Google Cloud. Responsibilities include managing Kubernetes, distributed data technologies, infrastructure as code, CI/CD pipelines, cloud governance, security, observability, and production reliability. The role evaluates architectures, automates infrastructure, monitors cloud deployments, leads technical initiatives, and mentors engineers while collaborating with trading, development, infrastructure, and operations teams.

Build and operate large-scale data ingestion, document processing, search, and serving systems for AI products. Responsibilities include handling massive heterogeneous datasets, designing schemas and indexes, enabling keyword, vector, and structured search, linking information across sources, and optimizing performance, reliability, latency, and cost. The role owns systems end to end, from source acquisition through user-facing functionality, and collaborates with AI researchers and product engineers.

Develop and maintain resilient, high-throughput Java services for the LMEsmart inter-office trading platform. Build automated testing frameworks, integrations, recovery workflows, and real-time performance reporting. Optimize JVM applications, manage test environments, support distributed systems and data streaming, and deploy cloud-native solutions using containers and Kubernetes. Collaborate with IT and business stakeholders while documenting application and infrastructure designs and improving DevOps and security practices.

Lead Salesforce and Node.js architecture, scalability, performance, security, and engineering excellence for enterprise applications. Manage and mentor a team of 10–15 engineers, set goals, conduct one-on-ones, hire talent, remove blockers, and partner with Product and Engineering leadership. Drive technical direction, integrations, deployment processes, developer experience, and reliable roadmap delivery across Salesforce and AWS-based services.

Set long-term technical strategy for backend identity systems, lead major distributed-system initiatives, ensure operational availability, establish engineering quality standards, collaborate cross-functionally, and mentor engineers. Build and launch highly available systems supporting international expansion, KYC verification, identity data management, decisioning, regulatory compliance, and global risk models.

Build and operate high-traffic backend services for Universal Wallet, supporting fiat and stablecoin deposits, withdrawals, balances, integrations, and settlement. Develop across Python and Kotlin/Java stacks, design APIs and event-driven systems, improve architecture and observability, review code, and participate in on-call production support. Collaborate asynchronously across teams, influence technical decisions, and deliver reliable financial infrastructure.

Designs and operates secure, scalable AWS infrastructure and reusable Terraform modules. Owns CI/CD, GitOps, observability, reliability, automation, and cost optimization. Builds production platforms for AI agents and LLM-backed services, including identity, access controls, secrets, evaluation, regression testing, and operational safeguards. Mentors engineers, communicates technical trade-offs, and supports broader platform and developer-experience strategy.

Drive enterprise sales of managed services and data security solutions across the UKI territory. Recruit and enable new MSP partners, grow existing relationships, build qualified pipelines, execute territory plans, and close net-new and expansion opportunities. Lead discovery, evaluations, proposals, and solution alignment with customers, partners, sales engineers, and technology alliances. Maintain forecasts, communicate value-based proposals, and provide customer and competitive insights.

Designs, builds, and maintains AWS and on-premises infrastructure for machine learning engineers, data scientists, and software teams. Responsibilities include operating Kubernetes and MLOps platforms, responding to service requests, troubleshooting production issues, implementing infrastructure as code, improving reliability and developer experience, and supporting secure, scalable product delivery. The role requires cross-team collaboration, clear technical communication, and commuting to London two days per week.

Designs and operates scalable data pipelines, warehouse models, and analytics solutions. Builds natural-language data interfaces using GenAI and LLM techniques, optimizes SQL, improves data quality and observability, and supports distributed data platforms on AWS or GCP. Partners with engineering, analytics, product, and business teams while contributing to standards, testing, documentation, code reviews, production support, and incident response.