Top AI Engineer Jobs in London

Design and build application-level AI features: implement RAG pipelines, prompt templates, retrieval strategies, and agentic workflows; integrate LLM-based functionality with legacy and multi-cloud systems; run structured evaluations, apply human-in-the-loop guardrails, and produce docs-as-code for handover to government engineers.

Hands-on Lead AI Engineer to design, build, and operate production-grade RAG, semantic search, embedding, and LLM orchestration systems in a UK government programme. Mentor engineers, implement Responsible AI guardrails, ensure security/observability, and enable capability transfer using Docs-as-Code and iterative delivery.

As a Software Engineer for AI Product at Figma, you'll build collaborative tools, lead AI projects, and work cross-functionally to enhance design accessibility and usability.

Lead design, build, and scaling of AI-powered features across the platform. Prototype, ship, and optimise end-to-end production systems; work closely with clinicians and customers; use analytics and user research to measure impact and prioritise work tied to commercial outcomes.

Build and deploy agentic systems and orchestration layers to accelerate engineering delivery. Develop and maintain an internal AI enablement hub, integrate agentic testing into CI/CD pipelines, evaluate build-vs-buy decisions, and drive cross-team AI initiatives to improve productivity and product quality.

As a Principal Forward Deployed Engineer, you'll deploy JetBrains Central in enterprise environments, implement AI governance, mentor teams, and advise customers on AI-native development best practices.

The Senior AI Engineer will design and implement AI systems, focusing on production-grade solutions using LLMs and agentic systems to meet business needs.

The Senior Software Engineer will enhance the reliability of AI systems, implement monitoring frameworks, manage failover systems, and lead incident responses, focusing on serving infrastructures for large language models.

As the primary engineer for Aperture OS, you will own the full codebase, drive feature development, manage API and AI integrations, and ensure security and scalability in a multi-tenant SaaS environment.

Build and ship AI-native systems for a fintech neobank: AI agents, RAG/LLM apps, tool-calling and automations to improve onboarding, KYC, support, risk review, CRM and operations, focusing on reliability, latency, cost, security and compliance while collaborating with product and engineering teams.

Architect and build a production, self-improving learning platform: capture event pipelines, measure outcomes, implement recommendation and feedback loops, maintain knowledge graphs and vector stores, build feature stores and experimentation infra, and create evaluation frameworks. Spend majority time coding and shipping resilient systems while providing technical leadership and shielding the engineering team.

Lead design and implementation of scalable AI-centric systems for offensive security, develop agentic AI capabilities (LLMs, RAG), own feature lifecycle to production, mentor engineers, run design reviews, and integrate AI into microservice architectures with cloud, container, and messaging technologies.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Own end-to-end design, build, deploy, and iterate on production AI systems that improve lead quality, reduce refunds, and automate workflows. Ship customer-facing AI features, agentic workflows, retrieval and context layers, reliable event-driven automations, evaluation datasets and monitoring, and reusable components while connecting technical decisions to business KPIs.

Design, build, and maintain developer tooling for the MAX AI inference stack, including profilers, tracers, debuggers, dashboards, and agent-native tools. Integrate tooling across MAX, Mojo, and driver APIs, collaborate with kernel/model/serving engineers, and drive performance analysis, debugging, and observability to improve developer productivity and production reliability.

Build core AI platform capabilities (agent runtimes, model routing, tool registries) and production-grade services and tooling using Node.js, TypeScript, NestJS and React. Develop reusable AI infrastructure, work with LLMs and LangChain/LangGraph, and own features end-to-end to enable secure, scalable AI across the company.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

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.

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, and optimize AI-driven automation workflows using platforms like Zapier/Make and LLMs. Integrate APIs, develop Python/JavaScript backend logic, maintain automation pipelines for sales/marketing/support, monitor systems, troubleshoot issues, and document scalable SOPs.

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 Full Stack Developer on the AI Team, you will build production-ready backend and full-stack features, integrating AI functionality into the think-cell product while collaborating with C++ and AI engineers.

The Principal AI Engineer will build and improve the core AI tutoring system, design evaluation methods, and collaborate on pedagogical goals, driving innovation in learner experiences.

Responsible for building and evolving an AI tutoring system, running experiments, collaborating with teams, and ensuring quality in model behaviors.

Build and evolve an AI tutoring system, run evaluations, design software, collaborate with teams, and ensure quality through testing and refinement.