Top AI Engineer Jobs in London

Design, build, evaluate, and deploy AI-powered software development workflows using Codex. Partner with engineering organizations to move from prototypes to reliable production systems, improve developer adoption and productivity, and address model behavior, integrations, security, reliability, observability, cost, and operational readiness. Lead technical workshops, diagnose implementation challenges, develop evaluation systems, and translate deployment insights into product feedback, reusable architectures, and technical resources.

Design and build a global, serverless AI inference platform running on heterogeneous GPUs and accelerators. Optimize model scheduling, request routing, caching, and load balancing for low latency. Improve reliability and observability, lead cross-functional projects, and mentor junior engineers.

Build production-grade agentic AI infrastructure for operational automation, including memory, context management, tool-calling, orchestration, evaluation, observability, and feedback loops. Develop reliable LLM workflows, coding-agent systems, and multi-agent architectures while partnering with operational and product teams to automate complex workflows across acquired agencies. Influence architecture, engineering standards, and reusable AI primitives in a remote, early-stage team.

Build and operate secure, reliable, and cost-effective AI platform infrastructure for language models and agentic workflows. Responsibilities include developing runtimes, services, developer tooling, observability, governance controls, deployment automation, and reusable platform capabilities. Partner with Security, IT, and engineering teams to integrate and scale the platform, support adoption, document best practices, and troubleshoot issues. The role also owns projects end to end and participates in on-call support.

Embed with product and customer engineering teams to identify bottlenecks, design and ship production solutions on the IFS Cloud platform, and improve developer velocity. Build workflows, integrations, event-driven systems, data pipelines, tooling, and reference implementations. Drive platform adoption, monitoring, internal developer platform requirements, and roadmap feedback while collaborating with architects, engineers, and business stakeholders.

Design and scale AI-driven solutions for fashion imagery and video, using computer vision, deep learning, generative AI, LLMs, and multimodal processing. Build reliable data pipelines, fine-tune and deploy models, standardize AI workflows, implement MLOps practices, develop evaluation frameworks, and partner with cloud infrastructure and product teams. Promote responsible AI by identifying and mitigating bias and risk while delivering production-ready, personalized shopping experiences.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Design, build, and operate reliable, scalable AI/ML platform infrastructure. Develop observability, resilience, security, and automation tooling, own NFRs, participate in on-call rotations, troubleshoot production issues, and collaborate cross-functionally to support large-scale AI deployments.

Design, build, and deploy Generative AI/LLM applications (chatbots, RAG, document intelligence) on GCP using Vertex AI, BigQuery, and containerised services. Work with data teams, implement CI/CD, monitoring, vector databases, and ensure Responsible AI, GDPR, and regulatory compliance while engaging stakeholders.

Build and deliver full-stack applications with generative AI and agentic capabilities. Collaborate with engineers, business professionals, clients, and end users to identify valuable AI use cases, design solutions, develop and test software, deploy applications, and iterate based on feedback. Write maintainable code, apply sound engineering judgment, evaluate AI-assisted development tools, and contribute to reliable enterprise workflows using APIs, retrieval, databases, and AI integrations.

Build and deploy production-grade AI agents, LLM applications, automated workflows, Python services, integrations, and scalable enterprise systems for internal tax teams. Responsibilities include API and event-driven integrations, workflow orchestration across Azure platforms and Copilot Studio, reusable connectors, CI/CD, monitoring, technical design, code reviews, and mentoring early-career engineers.

Customer-facing AI engineer delivering and productionizing GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, present at conferences, and collaborate with product and engineering to shape roadmap and influence priorities.

Build and maintain secure, scalable Flow Registry features for GitLab’s agentic platform using Python, LangChain, and LangGraph. Design GraphQL and REST APIs, improve automated testing, optimize PostgreSQL-backed applications, and collaborate across backend, frontend, product, UX, and AI teams. Participate in Tier 2 on-call support, troubleshoot production issues, improve observability, and contribute to responsible AI integration in GitLab’s remote, asynchronous environment.

Lead developer relations across EMEA AI Natives by advising partners, startups, and developers on NVIDIA technologies. Demonstrate AI and computing solutions, create technical enablement resources, guide onboarding and integrations, map ecosystem opportunities, and influence product roadmaps through partner feedback. Collaborate with engineering, product, marketing, sales, and legal teams while delivering technical talks, workshops, mentorship, and strategic developer programs.

Build and operate production AI systems for go-to-market teams, including account segmentation, scoring, enrichment, buying-signal detection, agentic workflows, prospect research, outbound personalization, call summarization, and CRM hygiene. Own solutions end to end from design through deployment, while optimizing quality, latency, cost, reliability, evaluation, monitoring, and guardrails. Collaborate closely with Marketing, Sales, RevOps, and system users to deliver measurable commercial impact.

Build and operate Kraken’s internal AI platform, including governed integrations, authentication, internal tooling, evaluation systems, spend controls, identity and access controls, provisioning, vendor configuration, and policy enforcement. Provide technical enablement through documentation, deep dives, and office hours while supporting teams and administering the enterprise AI estate.

Builds AI enablement infrastructure, including LLM cost observability, usage tracking, agentic coding guardrails, internal developer tooling, and AI tool evaluations. The role delivers production-quality code, automated tests, CI/CD improvements, code reviews, and end-to-end project ownership. It also establishes AI usage best practices and supports build-versus-buy decisions across infrastructure, tooling, and business processes.

Leads strategic AI and machine learning initiatives from concept through production. Designs scalable solutions connecting data, models, platforms, and systems; develops reusable engineering patterns; improves solution quality and reliability; conducts analysis supporting strategic decisions; builds stakeholder partnerships; and provides organizational leadership, mentoring, and innovation across the enterprise.

Conduct hardware-aware AI research: generate ideas, design and run ML experiments, implement performant scalable systems, publish and present results, and collaborate with researchers, silicon and software engineers to guide next-generation AI hardware.

Design, build, and operate enterprise-grade AI agents and agentic workflows. Integrate multi-provider AI platforms, implement orchestration, retrieval/RAG, observability, and IaC. Partner with clients to define use cases, run POCs, deploy scalable cloud-native systems, measure agent performance, and craft reusable patterns and documentation.

Build and own production-grade agentic AI systems end to end, including multi-agent orchestration, LLM and vision-language model integration, retrieval, memory, guardrails, evaluation, observability, and deployment pipelines. The role requires designing reliable systems for cloud, on-premises, air-gapped, and offline environments; handling large multimodal datasets; and setting technical direction for other engineers. Strong Python, software engineering, production AI/ML, and secure deployment experience are required.

As a Platform Engineer, you'll manage cloud infrastructure, enhance developer tooling, optimize CI/CD pipelines, ensure system security, and provide support for operations, contributing to team efficiency and productivity.

Build and maintain backend AI capabilities for GitLab Duo Chat: design flow components and agentic flows in Python/LangGraph, integrate LLMs and multi-agent orchestration, implement and maintain APIs in the Rails monolith, improve observability and reliability, and participate in on-call rotations and cross-functional collaboration to deliver safe, scalable AI chat features.

Lead the architecture and technical standards for production GenAI systems, including retrieval, evaluation, data pipelines, agentic workflows, reliability, cost, and latency. Build the most complex components, approve high-risk AI systems, establish shared engineering foundations, and mentor senior engineers. The role partners with engineering and product leadership, influences hiring and reviews, and translates emerging industry developments into practical architecture decisions.

Build and lead production-grade Generative AI software for document extraction, classification, translation, fraud detection, and workflow optimization. Own architecture, technical roadmaps, scalable services, model and prompt evaluation, testing, code reviews, and regulatory requirements including resilience, explainability, and auditability. Mentor engineers and collaborate with Risk, AI platform teams, and senior stakeholders in a regulated banking environment.