Top AI Jobs in London

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.

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 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.

Build and deploy production-grade autonomous AI systems (Digital Workers) for enterprises. Embed with clients to map workflows, integrate models, data, and infrastructure, and deliver scalable agentic solutions using LLMs, RAG, multi-agent coordination, orchestration, and cloud platforms.

Develop computer vision models for object recognition and video understanding while evaluating and improving their performance for real-world applications.

The AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

Design, build, and productionize AI/ML solutions within the Workday practice. Collaborate with data scientists and engineers to deploy scalable models, implement MLOps (CI/CD, versioning, monitoring), integrate with enterprise data systems and cloud platforms, and ensure reliability and performance of AI-driven features.

Build and deploy production-ready AI/ML systems across the full lifecycle, including agentic systems (RAG, prompt engineering), non-GenAI models (recommenders, forecasting), APIs/microservices, CI/CD and IaC, monitoring and guardrails, and cross-functional collaboration to ensure scalable, secure, and observable ML solutions.

Lead design and delivery of production LLM-powered agent systems and firm-wide agent standards. Build reference agent apps, connect agents to internal data/APIs, establish security and governance for AI, adopt deployment and observability infrastructure, and partner with stakeholders to identify high-value use cases.

Lead AI-focused presales pursuits for ERP/EAM/FSM, translate AI capabilities into domain-specific solutions, create demos and proof-of-value, mentor presales, shape GTM strategy, and collaborate cross-functionally to drive technical wins and portfolio adoption.

The Commercial Associate will engage in outbound sales strategies, account prioritization, and collaborate with Account Executives to drive opportunities, aiming for fast growth and skill development in a technical B2B environment.

Lead architecture and delivery of enterprise-scale data and AI projects on Google Cloud. Provide pre- and post-sales technical advice, design and migrate cloud solutions, mentor architects, collaborate with partners and sales, define best practices, and build next-generation ML products and scalable production-grade platforms.

Design, build and maintain modular, scalable, performant backend systems across the full software lifecycle; diagnose and resolve system issues; mentor junior engineers; collaborate with product and cross-functional teams; iterate quickly to deliver customer-focused features.