Top AI Engineer Jobs in London

Design, build, deploy, and own high-performance distributed data systems processing petabytes of robotics data and billions of items. Develop performant protocols and APIs, ensure reliability under heavy load, manage production services, and improve systems through testing, monitoring, code reviews, and careful architectural decisions. Collaborate with a high-performing team while solving complex data-intensive problems for AI and robotics applications.

Design, build and deploy agentic AI features and LLM-driven systems for secure, compliant environments. Integrate AI across backend and UI, optimise models (including perception/CLIP/CLAP), document for compliance, and deliver robust production solutions.

Build and scale shared AI platform capabilities on Azure, with emphasis on agentic AI runtimes, orchestration, tool integration, LLMOps, evaluation, observability, security, and reliability. Develop reusable templates, standards, and reference implementations for enterprise AI adoption. Partner with product, infrastructure, security, and engineering teams to establish governed model access, lifecycle management, CI/CD, and production-ready AI operating practices.

Lead design and delivery of production AI agent systems end-to-end: architecture, context/retrieval strategies, tool integrations, evaluation pipelines, and full-stack implementation. Drive technical direction, mentor engineers, and use Claude Code as the primary development workflow to embed AI across the company.

Advise law firms on AI adoption across billing and revenue workflows. Configure Intapp Celeste AI agents, coworkers, and playbooks around billing guidelines and client terms; map work-to-cash processes; identify automation opportunities and risks; train billing and finance teams; drive adoption; and provide product strategy feedback. The role requires law firm billing or revenue operations experience, practical AI and prompt engineering fluency, strong stakeholder communication, and client-facing judgment.

Develop and support scalable, cloud-native FX cross-border payments and custody solutions using Java and modern technologies. Responsibilities include architecture, coding, testing, production support, lifecycle improvement, stakeholder collaboration, mentoring, and implementation of automation and data-driven solutions. The role requires ultra-low-latency development, messaging, databases, caching, AI coding tools, and strong systems knowledge.

Build and productionize forecasting and machine learning models using weather, climate, and satellite data to generate agricultural, environmental, and risk signals. Own end-to-end pipelines from data ingestion and modeling through evaluation, orchestration, and customer delivery. Improve experiment design, uncertainty analysis, reproducibility, and monitoring while collaborating with science, engineering, product, and market intelligence teams.

Build and deploy practical AI tools, prototypes, applications, APIs, agentic workflows, and shared platform capabilities. Partner with business and engineering teams to drive AI adoption, improve solution performance and cost, and establish responsible AI governance. The role also involves evaluating emerging AI technologies, creating reusable patterns, mentoring colleagues, and mitigating AI risks across a regulated, multi-product organization.

Design and optimize model-serving architectures and inference pipelines for high-throughput, low-latency AI performance on mobile, edge, and GPU-cluster environments. Develop custom GPU kernels and Metal shaders, benchmark production systems, diagnose computational and memory bottlenecks, and implement techniques including parallelism, quantization, pruning, FlashAttention, KV caching, and speculative decoding. Collaborate cross-functionally to integrate scalable inference solutions and continuously improve performance.

Design and deliver data architectures, cloud platforms, pipelines, integration frameworks, and governance practices for regulated-industry clients. Advise on data strategies, operating models, lakehouses, warehouses, data mesh, and AI-ready ecosystems. Lead transformation engagements, support pre-sales and thought leadership, mentor consultants and engineers, and maintain expertise in emerging data, cloud, analytics, and AI technologies.

Guide UK enterprise customers through AI evaluations, proofs of value, procurement, and Claude deployment. Advise engineering teams on architecture, agent design, evaluation, security, compliance, and scale; present business cases to executive stakeholders; coordinate with account executives, architects, product, engineering, and systems integration partners; and provide technical guidance throughout implementation.

Experiment with generative image and video models, build advanced ComfyUI workflows and automated pipelines, and create high-quality demos, examples, guides, and social content. Translate technical experimentation into engaging developer- and creator-facing material, rapidly evaluate new model releases, and collaborate with Growth, Product, and Engineering on launches and use cases.

Own product features end-to-end, including architecture, integrations, technical specifications, deployment, testing, and production reliability. Use AI agents as the primary development tool, delegate implementation, critically review generated code, and ensure financial transaction reliability. Work independently as an individual contributor while helping evolve the company’s AI-first development process.

Partner with startups to identify high-impact Codex use cases, build AI coding workflows and integrations, resolve technical issues, and drive sustained adoption. Lead technical talks, demos, workshops, hackathons, and developer programs. Create reusable code examples, guides, and tools, while translating customer feedback into improvements for OpenAI’s Product, Research, and Engineering teams.

Design, build, and operate AI-native production systems and agentic workflows at scale. Integrate LLMs and observability to automate incident detection and remediation, maintain CI/CD and IaC pipelines, champion engineering best practices, mentor teams, and ensure compliance within a regulated financial environment.

Design, build, test, and maintain scalable full-stack web applications and integrations using Java, Python, and React. Implement AI-enabled features, event-driven data pipelines, and marketing integrations (CDP/CRM/CMS). Improve code quality via AI-assisted tools, CI/CD, observability, and security/compliance best practices while collaborating with product, marketing, and partners.

Serve as a technical advisor to high-growth companies building products with Claude. Partner with engineering teams through pair programming, architecture reviews, code contributions, prototypes, evaluation frameworks, documentation, and scalable AI application design. Create developer resources, identify cross-engagement patterns, support hackathons and workshops, and collaborate with Anthropic’s product and engineering teams. The role requires production LLM application experience, strong Python skills, customer-facing communication, and travel to customer sites.

Build and deploy scalable AI applications, intelligent agents, and AI-powered features in production. Collaborate with research, data science, and product teams; evaluate emerging AI technologies; and promote responsible AI through bias detection, explainability, and safety alignment. The role requires production machine learning experience, strong Python and AI framework skills, cloud and MLOps experience, and expertise in deep learning, NLP, and large language models.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows for enterprise customers. Own system architecture from proposal through deployment, develop React and TypeScript interfaces, and partner with DevOps on continuous deployment, monitoring, logging, performance, and security. Collaborate with product, data science, and engineering teams while mentoring peers and delivering resilient production software.

Build and deploy production-grade agentic AI applications, generative AI features, RAG pipelines, and LLM integrations for customer engagements. Develop Python and FastAPI services, model data with relational and graph databases, implement governance guardrails, and instrument systems for observability. Collaborate with platform engineering on deployment and MLOps/LLMOps, modernize legacy workflows, troubleshoot reliability issues, and contribute reusable AI patterns and best practices.

Own the backend and agent systems powering LLM products, including multi-turn conversations, tool calling, RAG, state management, evaluations, monitoring, and production reliability. Diagnose failures across models, knowledge systems, tools, and infrastructure; establish regression testing and quality measurement; and guide product and technical decisions. The role requires shipping and improving LLM features at scale, strong backend engineering, disciplined evaluation practices, and preferably Web3 experience.

Develop and evaluate LLM applications, agentic systems, and retrieval pipelines for maritime data. Partner with Product Managers to prototype AI solutions, define evaluation criteria, design architectures, and transition successful work into production. Build embedding and retrieval pipelines, evaluation suites, guardrails, observability, and responsible AI practices on Azure and Databricks. Communicate findings, contribute to shared tooling, and guide adoption of effective AI technologies.

Build and shape Sesame’s front-end experience, including dense financial interfaces, coded design systems, interaction patterns, and client journeys. Establish standards for AI-assisted development, review interface quality, improve consistency, and collaborate directly with investment professionals and advisors. The role combines senior front-end engineering with UX judgment, product thinking, performance optimization, mentoring, and ownership of the end-to-end product experience.

Own the quality strategy and end-to-end testing pipeline for an AI-generated product handling real-money transactions. Build automated UI, browser, mobile, pre-merge, nightly, release, and production testing gates; integrate testing with CI/CD; evaluate external tools; and improve reliability of critical financial flows. The role partners with engineering and DevOps, uses AI coding agents daily, and drives quality initiatives as an autonomous individual contributor.

Design, build, and deploy scalable AI architectures, pipelines, LLM integrations, RAG systems, and multi-agent workflows. Operationalize generative AI and NLP solutions in production, collaborate with data engineers on infrastructure and vector databases, and champion AI-assisted coding practices. Provide technical consulting to clients, mentor teams, and advise on AI product development, modern data stacks, synthetic data, and explainable AI.