Top AI Jobs in London

Lead design and delivery of secure, scalable AI solutions across enterprise programmes. Define AI architectures, governance, Responsible AI, and reusable patterns. Provide architectural oversight, support presales and discovery, build prototypes and accelerators, and guide delivery teams from discovery to production and ongoing model management.

Embed with client teams to design, deploy, and scale production AI platforms end-to-end. Deliver measurable business outcomes via rapid prototyping, productionization, governance, and stakeholder alignment while building reusable playbooks and patterns.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, LLM provider integration, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment, and reusable patterns while embedding with client engineering teams to measure and improve agent accuracy, latency, safety, and cost.

Design, build, and deploy production-grade multi-agent AI systems for enterprise clients. Own RAG pipelines, multi-LLM integration, LLMOps (eval, prompt governance, observability), orchestration frameworks, and lead client engineering engagements and engineering teams.

Lead end-to-end ERP implementations and customer relationships, deliver onboarding and go-live execution, shape product roadmap with product and engineering from customer feedback, establish implementation methodologies and standards, and drive best-practice ERP deployment for finance teams.

Serve as the technical bridge between customers and internal teams to design, validate, and onboard AI infrastructure solutions across bare metal, BMaaS, Kubernetes and cloud-native platforms. Lead technical discovery, POCs, benchmarks, architecture reviews, reusable reference architectures, and customer workshops while influencing product roadmap and production readiness.

Lead and grow a team of 5–8 AI engineers to build an enterprise AI platform delivering conversational, generative, and agentic AI features. Drive end-to-end execution, establish AI engineering best practices, maintain CI/CD pipelines, collaborate with product and platform teams, and communicate technical progress and risks to stakeholders.

Lead architecture and build an automation and AI-native toolchain (fusing traditional automation with agentic workflows), mentor teams, drive cross-department adoption, standardize practices, and evolve IT operations toward infrastructure-as-code and platform-first models.

Lead adoption and scaling of Generative AI and ML within Treasury, design and deliver AI/ML solutions (liquidity forecasting, capital optimisation, risk), migrate systems to secure scalable cloud, champion data governance, ensure automated testing, and mentor engineers while aligning stakeholders and driving innovation.

Lead customer-facing data science and AI projects for commodity markets: identify high-impact opportunities, prototype and deploy ML/AI solutions (including LLMs and agentic approaches), measure business impact, collaborate with product, engineering and domain experts, and share learnings to shape ICIS data and AI products.

Hands-on Staff Engineer who designs, builds, and deploys AI agents and LLM-based automation to redesign and automate the deal approval process. Integrate solutions with CRM, ERP, and finance data sources, own delivery end-to-end from prototype to production, and drive AI adoption and governance within Commercial Finance to reduce cycle time and manual work.

Senior client-facing consulting leader for Energy & Infrastructure who originates and leads AI- and data-enabled transformation engagements across UK/EMEA. Drives go-to-market, shapes solutions from discovery through delivery, leads multidisciplinary teams, owns P&L and pricing, and grows pipeline, revenue and margins while delivering measurable operational and commercial value for E&I clients.

Lead AI and RPA-driven productivity improvements across Automation & Digital. Design and pilot scalable workflows, run Kaizen/DBS workshops, develop KPIs/dashboards, analyze operational data to drive efficiency, and enable cross-functional adoption to increase throughput and reduce operational costs.

Lead development and deployment of multimodal neural search and ranking models for personalized learning. Conduct research, prototype and productionize deep learning NLP/IR solutions, collaborate cross-functionally, mentor junior scientists, and establish ML best practices.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.

Lead discovery and shape AI product strategy by identifying high-value AI opportunities, creating business cases and roadmaps, mobilising cross-functional teams, validating technical feasibility, and establishing governance to deliver scalable, measurable AI solutions.

Evaluate and test AI chatbots and language models via structured written and voice interactions. Rate outputs, submit write-ups, screenshots, or recordings, and follow project-specific statements of work. Work is task-based, remote, flexible hours, and may involve audio recordings and iterative batches.

Lead product marketing for AI and emerging products across the full lifecycle: market research, positioning, pricing, launches, enablement, and growth. Drive GTM strategy, alignment with Product/Sales/CS, and build repeatable frameworks. Use customer insights and AI tools to inform roadmap, messaging, and commercial decisions while scaling adoption and go-to-market execution.

Lead end-to-end delivery of Markets Operations AI transformation workstreams—AI customer queries, controls automation, agentic and low-code automation—managing integrated plans, risks, benefits, governance, stakeholder alignment, change adoption, and benefits realisation across engineering, operations, risk and product.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

Work as a consultant combining human insight, strategy and AI to deliver client insight programmes. Synthesize diverse evidence, apply methodology and emerging technologies, support proposals and business growth, and develop consultancy and analytics skills across sectors.

Lead and deliver AI-enabled insight programs, manage client accounts, combine human, cultural and artificial intelligence methods, grow client relationships and revenue, coach and develop teams, shape propositions and ways of working to evolve insight consultancy.

The Product Owner (AI) will lead Nevis' AI-driven product development, defining requirements, managing cross-functional teams, and enhancing user experience for financial advisors.

The Applied AI Engineer will design and build LLM-driven automation products, collaborate on AI capabilities, and improve AI performance for wealth management solutions at Nevis.

The software engineer will develop AI tools for sustainability reporting, collaborating on product strategy and ensuring code quality while leveraging LLMs.