Top AI Engineer Jobs in London

Lead design and implementation of scalable, cloud-native APIs, services, and accessibility-first UIs. Modernize legacy systems into API-first, event-driven components, deliver AI-native features across front-end and back-end, own reliability/observability, champion DevOps/CI-CD, mentor engineers, and drive architectural and quality improvements from design through production.

Own end-to-end delivery of production ML and LLM-powered systems: design and build training/deployment pipelines, package and deploy models, implement monitoring for performance and drift, support batch and real-time workloads, evolve internal ML platform and tooling, promote best practices, and provide technical leadership and mentoring across data science and engineering teams.

Lead development of AI-native backend services, APIs and developer tooling to enable LLMs, AI agents and RAG-driven features. Integrate generative AI, ensure observability and safe agent interactions, collaborate across teams, perform code reviews, and improve platform scalability and developer productivity.

Build and maintain backend services, APIs and developer tooling to enable AI-enabled applications and agent-ready platforms; integrate LLMs and RAG, implement agent orchestration and MCP, and contribute to AI observability, testing and model evaluation in collaboration with engineers and product teams.

Design, build, and maintain scalable, fault-tolerant serving platforms for LLMs. Integrate models for production and research, implement monitoring and observability, perform architecture reviews, and lead cross-functional end-to-end projects enabling LLM capability discovery and high-performance serving.

Lead the evolution of Tenable's multilingual delivery platform toward AI/LLM-led localization. Design event-driven, API-first translation pipelines, integrate LLMs into production workflows, extend AI-led delivery across content types, define standards and governance, build AI-powered reporting, and deliver key initiatives while coordinating distributed and contracted engineering partners.

Design, build, and operate a secure, high-performance sandboxing platform and client library for code execution across containerized and virtualized environments. Ensure strong isolation, optimize latency and resource use, debug and reduce errors, respond to incidents, and drive roadmap and architecture reviews while partnering with internal users.

Build and ship production-grade AI features inside established C# .NET products: prompt and context design, LLM integration, evaluation, error handling, monitoring, and collaboration with product and engineering teams to deliver accurate, safe, measurable AI capabilities.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

As a Senior AI Engineer at Savanta, you will develop production-grade AI applications using C#, .NET, and Azure while collaborating with cross-functional teams to implement AI-driven features and enhance engineering capabilities.

Build production AI agents, LLM apps, RAG and retrieval-based systems, tool-calling and automations for fintech workflows (onboarding, KYC, risk, CRM). Ship, evaluate, and improve reliable AI-enabled systems with product and engineering teams, addressing latency, cost, security and compliance.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

As a Senior Python Engineer, you'll design and build the core infrastructure for AI forecasting systems, integrating data ingestion and reasoning mechanisms while contributing to technical strategy and team growth.

As an AI Deployment Engineer, you will work with startups to optimize AI systems, prototype solutions, and improve model behavior while collaborating closely with product and research teams.

As an AI Solution Engineer, you'll identify AI opportunities, assess feasibility, design prototypes, and collaborate across teams to innovate solutions using AI technology in infrastructure.

Build infrastructure and tools to evaluate and deploy AI/ML models for scientific data, partner with product managers and scientists, develop data systems, implement and evaluate ML algorithms, own features end-to-end, and contribute to architecture and development practices.

Deploy and optimize LLM-based solutions for real-world government, healthcare, and energy problems. Build scalable data pipelines, productionize models (RAG/agents), monitor performance, engage stakeholders including government officials, and collaborate with cross-functional teams to deliver impactful AI products.

Build, deploy, and maintain AI-powered web applications for government and industrial customers. Work end-to-end: define product specs with customers, implement front-end and back-end systems, integrate data pipelines and sensor networks, optimize performance and reliability, and support deployed systems globally. Collaborate with cross-functional teams, mentor junior engineers, and travel on-site to deliver production-grade solutions.

Develop intelligent agents for a materials discovery engine, orchestrating scientific workflows and integrating experimental design for sustainability solutions.

Support sales by designing and demonstrating Cisco cloud, data center, and AI-ready infrastructure solutions. Engage customers to assess modernization goals, craft platform architectures, present technical value, run workshops, and collaborate with account teams and partners to drive adoption of compute, storage, networking, and cloud-native technologies.

Validate and automate testing for AWS serverless, event-driven and modernized applications. Design and run functional, integration, contract, performance, resilience and security tests. Build automated test frameworks, integrate with CI/CD, leverage AWS Kiro for AI-assisted test generation and validation, and support migration/regression testing across microservices.

Build and maintain product features that streamline property transactions. Use LLMs and AI coding agents to accelerate development, debug issues, and iterate rapidly. Own tasks from day one, collaborate with a small team, and apply practical problem-solving to deliver customer-facing improvements.

Design, build, and deploy production-scale AI systems including multi-agent workflows, RAG pipelines, and fine-tuned LLMs. Deliver full-stack web and backend integrations, develop evaluation frameworks and monitoring, iterate prototypes into production, and shape internal best practices for applied AI.

Design, build, and maintain Python-based AI applications and intelligent workflows. Integrate AI/ML models and LLM APIs, implement APIs and microservices, optimize data pipelines, ensure scalability, security, CI/CD, and collaborate across teams to deliver production-grade AI features.