Top AI Jobs in London

Design and build scalable AI-powered backend services and APIs in Golang for cloud-native, microservices-based platforms. Develop developer tools, ensure observability, CI/CD, and performance, solve distributed-systems challenges, and mentor engineering teams to deliver production-ready solutions.

Drive end-to-end research and engineering on vision-language and multimodal models: data curation, training pipelines, post-training workflows (fine-tuning, distillation, RLHF), efficiency and deployment optimizations, distributed GPU training, evaluation frameworks, and publishing research.

The Applied AI Engineer will design, build, and deploy LLM-powered AI features, improving workflows while engaging directly with customers to debug and enhance the system.

As a Staff/Senior AI Engineer, you will develop production-ready coding agents and improve AI model capabilities for JetBrains' software development tools. Collaborating with teams, you'll focus on model training, evaluation, and product integration to enhance user experience.

Lead and advise clients across UK and Europe on applied AI: identify and prioritise high-value use cases, design and run POCs, translate AI into commercial impact, guide engineering to production, support business development, manage distributed teams, and ensure responsible, scalable AI and data foundations.

Hunter sales role selling IFS Loops, an agentic AI platform, into industrial enterprises. Drive net-new revenue, lead consultative conversations about autonomous workflows and decision intelligence, engage senior business stakeholders, and partner with account teams and solution engineers in a matrixed GTM model.

Lead the development and integration of AI solutions, focusing on advanced modeling, generative AI architecture, and building user interfaces for decision-making tools.

Lead design and delivery of LLM platform infrastructure across AWS and on-prem GPU environments. Own roadmap, optimize training/inference, establish model/data lifecycle, drive knowledge graph strategy, coordinate cross-functional teams, select vendors, and define governance and platform engineering standards.

Lead design, build and deploy enterprise-scale AI products: agentic workflows, RAG, LLM integrations, vector DBs, APIs and microservices. Define architecture, reliability, observability and governance, mentor engineers, partner with product and data teams, and drive AI platform adoption and measurable business outcomes.

Hands-on lead full-stack engineer who designs scalable architecture, ships production SaaS features, integrates AI tools, mentors senior engineers, improves deployment pipelines, and drives engineering quality.

Lead the Large Enterprise AI Deployment Engineering team across EMEA to drive GenAI use cases into production. Manage customer technical journeys, align engineering and product roadmaps, mentor ADEs, and serve as primary technical escalation and industry thought leader.

Deliver production-ready ML model releases: train and iterate PyTorch models, evaluate and debug performance, apply optimizations (quantisation, distillation), collaborate with performance and deployment teams, and ensure models meet tight on-vehicle runtime constraints.

Lead the Central AI Team and own the firm's AI transformation portfolio: define strategy, governance, prioritisation, readiness, value measurement and scaling. Align business, legal, technology and data stakeholders, drive board-level decisions, manage dependencies and risks, and build organisational capability for enterprise AI adoption.

Lead development of network and AI strategy, governance and roadmaps. Identify, prioritise and scale AI use cases for network planning, operations and assurance. Ensure safe, auditable deployments and partner across security, data, procurement and operating companies to deliver measurable network and business outcomes.

Lead go-to-market strategy and commercial execution for a new AI-native Market Research service. Build positioning, pricing, and metrics; generate pipeline and close deals; partner with Sales, Marketing, Product, and Research; represent Maze publicly; and help scale the service and team while feeding market insights back into product and offering design.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Deploy and maintain AI enablement infrastructure (MCP servers, AI agents, hosting) and cloud environments on AWS/GCP. Build IaC and CI/CD pipelines, monitor performance/cost, support containerized services, manage API keys and permissions, document runbooks, and collaborate with Security, Engineering, and stakeholders to improve cloud governance and AI tooling adoption.

Build and evolve a real-time AI tutoring system: design prompt architectures and agentic workflows, run experiments and custom evaluations, integrate models with platform infrastructure, prototype production model interactions, ensure safety and observability, and collaborate with educators and product teams to translate pedagogy into technical solutions.

Lead design and delivery of production-grade ML and GenAI solutions on the Databricks platform. Architect end-to-end ML pipelines, optimize training/inference, implement MLOps/LLMOps, build MVPs for sales, influence product roadmap, mentor peers, and present technical content externally.

Review, evaluate, and annotate AI-generated content (text, audio, images, video). Identify errors and quality issues, select best outputs, provide written feedback, follow detailed guidelines, attend trainings, and complete tasks remotely within deadlines.

Own and grow the Lesson Insights product end-to-end: prioritize actionable AI-driven feedback, connect per-lesson insights to long-term progress, involve tutors, scale across languages, increase engagement and retention, and run data-driven experiments and analyses.

Lead and grow a high-performing engineering team building scalable, customer-facing products. Own engineering delivery, architecture, code quality, observability, incident response, and engineering culture. Partner with product, design, mobile, backend and AI teams to turn ambiguous problems into production systems while coaching engineers and improving processes to deliver reliable, high-quality outcomes.

Lead and manage a cross-functional engineering team to design, build, and operate a high-scale payments app. Drive architecture, delivery, code quality, reliability, observability, and incident response while coaching engineers and improving processes to ship customer-focused products quickly and reliably.

Lead technical design and execution of products for an AI-enabled payments app. Hands-on coding, architecture, system design, code review, debugging, production ownership, mentoring engineers, improving dev tooling, and collaborating with product, design and AI teams to ship reliable, scalable systems.

Lead technical design and delivery of AI-native email applications. Own architecture and implementation across backend, frontend, APIs, databases and cloud. Stay hands-on writing and reviewing code, debugging production, mentoring engineers, driving major initiatives, improving tooling, and ensuring reliability, security and observability while collaborating with product, design and AI teams.