Top AI Jobs in London

Manage the GTM AI Engineering team overseeing automation, AI agent strategy, and integration with sales and marketing for revenue growth.

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 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 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.

Lead and manage a cross-functional engineering team to design, build, and operate scalable, reliable payment and AI-integrated products. Own technical architecture, delivery, code quality, observability, incident response, and engineering processes while coaching engineers and driving product-focused outcomes.

Technical lead responsible for designing, building and operating scalable full-stack systems. Hands-on coding, architecture decisions, code reviews, mentoring, cross-team collaboration, production reliability, observability and driving features from concept to production.

Lead and manage a cross-functional engineering team to design, build, and operate scalable, reliable production systems. Own technical architecture, delivery, code quality, observability, incident response, and engineering processes while coaching engineers and driving product outcomes in partnership with product, design, mobile, backend and AI teams.

Lead and manage 2-3 AI POD teams to drive pipeline, revenue, margin, and adoption. Coach and develop sales specialists, run GTM operating cadence, govern pipeline/CRM, engage in executive customer pursuits, coordinate shared resources and OEM partners, and integrate POD work into regional account and territory strategies.

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 manage 2-3 AI POD teams to drive pipeline, revenue, margin, and adoption. Coach and develop sales specialists, run GTM cadence, govern CRM and forecasts, engage at executive customer levels, coordinate shared and OEM resources, and integrate PODs with regional sales and services.

Design and deliver AI-driven proofs of concept to improve customer experience and operational efficiency. Rapidly prototype, test, and iterate solutions; partner with stakeholders to translate complex problems into experiments and recommendations; shape pragmatic AI strategy and promote a culture of innovation.

The Lead Engineer will develop secure systems, ensure engineering best practices, lead technical architecture, and integrate AI capabilities into production systems.

Lead product for developer-focused IDP and agentic AI platform capabilities: own discovery, roadmap, and outcomes; champion developer experience (APIs/SDKs/docs/onboarding); translate customer needs into clear requirements; partner with engineering and architects; use AI to accelerate product discovery and delivery; mentor other PMs and set team standards.

Lead and scale Mimica's product organisation, define global product strategy and roadmap for AI-driven task/process mining and automation, mentor product and design teams, improve product operations and discovery, align cross-functional stakeholders, handle escalations, and hire and forecast team needs.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Lead end-to-end product design for complex, data-intensive workflows across web, desktop, and mobile. Run user research and validation, craft flows, wireframes, prototypes, and polished interfaces. Partner with product and engineering, use data and design judgment to improve outcomes, and contribute to design systems, patterns, and cross-team collaboration. Advocate for AI-enabled UX where applicable.

Lead end-to-end AI talent placement in London: source and screen AI-skilled candidates, build employer relationships, manage placement lifecycle and retention, hit placement targets (5-8/month), run diversity outreach (40%+ women), maintain candidate records and reports, and provide market intelligence to improve UtivaHire and UtivaLearn.

Lead customer success initiatives by managing client relationships, driving user adoption and satisfaction, and influencing product development based on customer feedback.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

Join Tether as an AI Research Engineer focusing on model compression and quantization for AI systems. You'll innovate in reducing model footprint for multimodal architectures while applying advanced compression techniques, ensuring high performance on edge devices.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Lead design and delivery of production-grade AI systems and Digital Workers. Embed with enterprise clients to translate workflows into scalable agentic architectures combining LLMs, RAG, multi-agent orchestration, retrieval, and cloud integration. Own end-to-end technical delivery, guide engineering teams, set technical direction, and ensure reliability and performance in production.