Top AI Engineer Jobs in London

Build and operate auditable multi-agent systems on Cloudflare’s platform for infrastructure forecasting, supply planning, cost modeling, approvals, and decision analysis. The role involves designing durable workflows, integrating statistical and machine learning models, creating BigQuery pipelines, enforcing risk controls and auditability, and authoring technical specifications and architectural standards.

Build production software and infrastructure for reliable, governable AI systems in customer-controlled environments. Responsibilities include developing agentic workflow components, services, APIs, evaluation pipelines, developer tools, permissions, observability, and deployment systems. The intern will debug distributed services, write maintainable code, participate in reviews, collaborate across teams, and communicate technical decisions clearly while working on a hands-on project with mentorship.

Lead the architectural evolution of an AI platform from monolithic to distributed systems. Design agent-ready services, write production Python, build distributed compute and workflow orchestration, and own CI/CD, testing, deployment, observability, reliability, and scaling. Mentor engineers, establish technical standards, guide cross-team decisions, collaborate with stakeholders, and support hiring. The role requires deep distributed systems, Kubernetes, AWS, workflow orchestration, data processing, security, and production reliability expertise.

Build and scale a distributed platform supporting engineering simulations, agentic applications, and physical AI workflows. Responsibilities include designing distributed architecture, developing production Python services, optimizing distributed compute, implementing workflow orchestration, and contributing to CI/CD, testing, deployment, observability, reliability, and incident response. The role also involves mentoring engineers, making technical decisions, collaborating cross-functionally, interviewing candidates, and supporting end-to-end platform operations.

Build and maintain internal AI systems, including machine learning and generative AI pipelines for training, deployment, monitoring, knowledge management, data analysis, automation, and decision support. Optimize scalable AI infrastructure, integrate and transform data, and collaborate with data scientists and business teams. The role requires full-stack software development, cloud and Kubernetes deployment, generative AI expertise, and end-to-end application ownership.

Develop and prototype domain-specific AI solutions for strategic enterprise customers. Lead technical discovery, proof-of-value projects, and secure scalable LLM or agent systems using RAG, tools, and guardrails. Integrate solutions with enterprise data and compliance frameworks, present business impact to executives, support presales and value realization, and contribute reusable AI methodologies. The role requires software and data engineering, production ML/LLMOps, AI architecture, business process expertise, and strong executive communication.

Leads software engineering within an agile enterprise technology team, designing, developing, testing, securing, and troubleshooting production systems. Promotes validated AI-assisted coding, code review, testing, and incident analysis practices; improves automation and operational stability; and evaluates vendor and internal architectural solutions. Requires advanced programming expertise, SDLC proficiency, cloud-native experience, application resiliency and security knowledge, agile experience, and financial-services IT knowledge.

Leads software engineering strategy and development for AI/ML data platforms. Designs reusable frameworks, architects governed agentic AI development workflows, builds secure and scalable cloud-native applications, and operates 24x7 tier-1 services. Influences technical, product, and business leaders; drives adoption of engineering methods, responsible AI controls, automation, and platform capabilities across teams.

Lead the design, development, deployment, and continuous improvement of AI/ML solutions for enterprise customers. Build integrations using Python or JavaScript, APIs, microservices, and enterprise systems; analyze data; automate workflows; address AI risk, privacy, and compliance; and align technical solutions with business goals. Mentor engineers and communicate effectively with technical and non-technical stakeholders in complex, ambiguous environments.

Build and operate production AI and rule-based agents for lead generation workflows. Responsibilities include integrating agents with data warehouses, internal services, and APIs; implementing structured outputs, validation, permissions, guardrails, audit logging, autonomy controls, evaluation, tracing, failure handling, and performance monitoring. Collaborate with operators, data scientists, product managers, and users to deliver dependable, measurable, and commercially valuable agent systems.

Develop and support cloud-native Java applications for cross-border FX payments and custody. Responsibilities include system architecture, feature development, testing, production support, lifecycle improvement, stakeholder collaboration, mentoring, and best-practice implementation. The role requires expertise in Java, Spring, SQL, messaging, caching, ultra-low-latency development, AI coding tools, and scalable production systems, with FX knowledge preferred.

Develop production-scale generative AI, machine learning, data, and analytics solutions for FX sales and trading. The role involves building Python-based applications and pipelines, integrating data governance and access controls, collaborating with traders, quants, and stakeholders, supporting production systems, improving development practices, delivering large-scale projects, and mentoring junior engineers. Financial-market knowledge, modern Gen AI technologies, databases, messaging systems, and cloud-native development are valuable.

Develop and support cloud-native Java applications for cross-border FX payments and custody. Responsibilities include architecture, coding, testing, production support, system modernization, scalability, CI/CD improvement, stakeholder collaboration, and mentoring junior engineers. The role requires expertise in Java, Spring, databases, messaging, caching, ultra-low-latency development, AI coding tools, and payment or FX technology.

Designs, develops, tests, deploys, and supports cloud-native applications and distributed systems using AWS, microservices, APIs, event-driven architectures, and serverless technologies. Leads technical design, modernization, integrations, migrations, production support, reliability, security, observability, and CI/CD improvements. Develops AI-enabled solutions, mentors engineers, evaluates technologies, contributes to architecture and standards, and coordinates complex go-lives across cross-functional teams. The role requires strong Python and AWS expertise, with experience in databases, messaging, testing, and enterprise application modernization.

Designs, develops, deploys, maintains, and scales AI-powered applications, with a focus on large language models and generative AI. Responsibilities include supporting existing applications, building new solutions, testing with end users, researching datasets, applying statistical and machine learning techniques, evaluating and fine-tuning models, and tracking AI advancements. The role requires strong Python development, front-end experience, collaboration, communication, and production-quality engineering practices.

Conduct penetration testing across infrastructure, applications, APIs, networks, Active Directory, cloud environments, and client-facing platforms. Assess AI solutions and agents for prompt injection, data leakage, excessive permissions, and guardrail bypasses. Support secure AI development, red-team exercises, access-control reviews, remediation, retesting, security sign-off, and technical and management-level reporting.

Develop and maintain cloud-native Java applications for cross-border FX payments and custody. Responsibilities include system architecture, hands-on development, testing, production support, lifecycle improvement, stakeholder collaboration, and mentoring junior engineers. The role requires building scalable, low-latency systems using messaging, databases, caching, CI/CD, and AI coding tools while supporting modernization and legacy-system replacement.

Build and operate scalable cloud data pipelines for quantum chemistry datasets used to train AI models. Responsibilities include transforming quantum chemistry software into cloud workflows, managing and validating scientific data, deploying containerized infrastructure through infrastructure as code and CI/CD, and ensuring reliability, security, observability, incident response, and capacity planning. The role collaborates with scientific and technical experts across disciplines.

Design, build, and operate production AI agents for investment and business functions. Translate business needs into agentic solutions using Databricks, Claude API, tool use, RAG, and enterprise integrations. Establish evaluation, testing, monitoring, governance, security, and regulatory compliance. Set AI engineering standards and coach Data and AI Engineering colleagues.

Build and operate an Azure and Databricks data platform, integrating enterprise systems such as SAP and Salesforce. Develop Terraform-based infrastructure and CI/CD pipelines, administer workspaces, environments, Unity Catalog, identity, networking, and storage, and provide secure AI capabilities including API layers, model serving, and LLM access. Own platform security, monitoring, cost control, compliance, documentation, and engineering standards.

Design and build generative AI applications for complex financial and legal workflows. Lead the full model lifecycle from experimentation and training through testing, monitoring, and deployment. Apply advanced techniques across information retrieval, knowledge graphs, recommendation systems, statistical modeling, and GenAI. Collaborate cross-functionally, translate business problems into scoped initiatives, mentor teammates, promote AI adoption, and deliver production-ready solutions with strong product impact.

Build production generative AI systems for complex financial and legal workflows. Responsibilities include document understanding, structured information extraction, model lifecycle management, retrieval and context engineering, data validation, evaluation, deployment, and monitoring. The role requires collaboration across teams, mentoring, product focus, and applying advanced AI research to convert legal and financial documents into reliable structured data.

Own the end-to-end ML adaptation toolchain, including data curation, foundation-model-assisted labeling, human-in-the-loop QA, active learning, training, evaluation, model promotion, and on-premise deployment. Lead development of self-serve workflows that protect customer data, provide dataset provenance and quality controls, identify field failures, and enable non-expert customers to adapt models independently. Grow a specialist team supporting curation, auto-labeling, deployment, and onboarding.

Own and optimize the C++ inference runtime for QVAC’s local AI stack. Deploy and enhance engines such as llama.cpp and ggml across edge devices and GPU architectures, improving startup, memory usage, latency, throughput, reliability, and long-session stability. Collaborate with researchers to productionize models, integrate AI features, and develop core runtime abstractions for private, on-device AI products.

Drive AI-led transformation across Legal, People, and FP&A by identifying workflow pain points, mapping end-to-end processes, and building reliable AI agents, automations, integrations, and internal tools. Advise on build-versus-buy decisions and technology stacks while implementing security, privacy, compliance, monitoring, human oversight, evaluation, and audit controls. Partner with frontline teams, executives, and engineers to deliver and continuously improve internal AI products.