Top AI Jobs in London

The Senior Full-Stack Engineer will build and maintain high-performance frontends, manage backend data systems, and ensure optimal data transfer/storage while working in Physical AI and robotics environments.

Lead design, development, and deployment of production-grade generative AI systems; create strategic AI roadmap; implement LLMOps best practices; build and mentor an AI team; evaluate and integrate third-party AI services; ensure scalability, reliability, security, and measurable impact of GenAI solutions.

Lead the design and delivery of GenAI solutions, leveraging LLMs and RAG architectures while integrating with enterprise systems. Focus on production-grade implementations and collaboration across teams for digital transformation efforts.

Lead the product team at Zego, driving strategic direction and AI integration, managing cross-functional programmes, and mentoring PMs to enhance product impact.

The Data Management Lead will oversee data management initiatives for AI transformation, focusing on data quality, governance, and stakeholder engagement while collaborating with various teams.

Build and operate production agentic AI systems and LLM-powered features. Implement RAG pipelines, extend shared AI infrastructure, monitor and debug production models, and collaborate with product and design to deliver secure, reliable AI capabilities in a regulated financial environment.

Design, build, and operate production-grade agentic AI and LLM-powered features and RAG pipelines over sensitive financial data. Collaborate with product and design, contribute shared AI infrastructure (LLM services, orchestration, evaluation), and own reliability, security, auditability, and compliance in production.

Lead strategy and roadmap for AI platform tools, integrations, APIs, gateways, and agentic capabilities. Partner with engineering, data science, and partners to build reusable services, drive GenAI/agentic use cases, define metrics, and balance short-term delivery with long-term scalability and adoption across Amex's digital ecosystem.

Build and ship production-grade agentic AI systems and LLM-powered features. Architect RAG pipelines over sensitive financial data, contribute shared AI infrastructure, own production reliability and safety, and collaborate with product and design to deliver customer-focused outcomes in a regulated environment.

Design, develop, and deploy production-ready ML/AI solutions including data ETL, exploratory analysis, modeling, ModelOps/MLOps, and GenAI/RAG systems; mentor junior staff and coordinate with stakeholders.

Lead design, development, and productionization of ML/AI solutions. Manage ETL, databases, model lifecycle, and MLOps pipelines. Perform EDA, statistical analysis, and monitoring. Mentor junior staff, run proofs-of-concept, communicate results to stakeholders, and ensure production-grade, auditable, scalable code and models.

Support development and execution of SEO, GEO and AI Search strategies. Conduct SEO and AI search visibility audits, analyse AI-generated brand representation, create content and entity optimisation strategies, apply technical SEO best practices, deliver training and workshops, support new business and thought leadership, and monitor AI search developments to adapt client strategies.

Own and productionize an internal AI agent platform: build agent workflows, frontend UIs, and Python/FastAPI backends; implement observability and evaluation (Ragas, Langfuse); integrate cross-cloud systems; mentor engineers and drive durable, testable, regulated fintech automation.

As a software engineer in generative AI, you will design and develop secure applications, optimize APIs, and collaborate with cross-functional teams to enhance AI solutions.

Lead AI projects in Clinical Development for Life Sciences. Engage with clients, manage teams, design solutions, and shape a new practice.

Lead CLM & Document AI sales motion with field teams, driving strategy from discovery through proof-of-value and close. Advise on enterprise contract intelligence, co-sell in complex deals, enable account executives, build pipeline and forecasts, codify best practices, and feed market feedback to product and marketing.

As an AI Research Engineer at Helsing, you'll architect and enhance deep learning frameworks, optimize infrastructure for distributed training, and develop strategies for large datasets while collaborating with a cross-functional team.

As a Deployed AI Engineer, you'll integrate software into systems, collaborate on customer requirements, and troubleshoot issues while showcasing AI capabilities.

You will design, train, and deploy agents in multi-agent environments, enhancing our reinforcement learning capabilities and integrating cutting-edge AI into production systems.

The Managing Consultant will lead data science initiatives, drive business growth in financial services, and build client relationships while managing teams and delivering high-impact solutions.

Embed with client teams to map and redesign creative workflows, build and deploy AI-powered pipelines integrated with DAMs/CMS and martech. Lead prompt engineering, agent workflows, automation, governance, training, and metrics tracking. Deliver proofs of concept and production-ready solutions, enable adoption, and develop reusable accelerators and practice methodologies.

Embed with client teams to diagnose workflow friction, redesign operating models, and deliver AI-powered proofs of concept. Map current-state processes, build working LLM-based prototypes using low-code/no-code tools, drive adoption through change management, measure impact, and support rollout to realize business value.

As a Senior AI Engineer, you will develop AI solutions, integrate models, and collaborate with teams to explore emerging AI frameworks and technologies. Responsibilities include working on client projects, employing agile methodologies, and contributing to an innovative tech community.

The AI Product Manager will oversee AI implementation in Conversational Commerce, ensuring quality and safety in AI deployment while driving product strategy and evaluation metrics.

The role involves developing, fine-tuning, and optimizing large language models, building ML infrastructure, and implementing CI/CD practices for AI/ML systems.