Top AI Jobs in London

Owns end-to-end enterprise AI sales for Cloudera’s supported Mistral offering and Anywhere Cloud. Responsibilities include pipeline development, strategic account planning, outbound prospecting, proof-of-concept leadership, technical and executive selling, deal negotiation, forecasting, and expansion revenue. The role coordinates engineering, product, legal, implementation, partners, and account teams while positioning AI solutions within customers’ data strategies and hybrid infrastructure.

Lead and scale an AI-driven creative design team producing performance marketing assets for paid media. Own creative direction, production workflows, quality control, testing, and performance-driven iteration. Develop AI tool expertise, prompt and style libraries, and automated workflows with engineering partners. Collaborate with marketers, media buyers, copywriters, producers, and video editors while remaining hands-on with design and AI tools.

Build and lead n8n’s internal AI and automation enablement function. Run recurring workshops, office hours, build-alongs, bounty hunts, hackathons, and dogfooding initiatives for technical and non-technical teams. Develop reusable templates, measure AI fluency and adoption, support cross-functional champions, and act as product owner for a safe internal automation playground. Partner with IT, Security, Data, and Solutions Engineering to define access, credential, and environment requirements.

Build and operate high-throughput backend services for financial crime detection across WorldRemit and Sendwave. Responsibilities include developing alerting, screening, risk-rating, and agentic AI investigation workflows; integrating third-party compliance platforms; improving observability, reliability, and auditability; collaborating with compliance teams; reviewing designs and code; and participating in on-call production support. The role requires strong Java and Spring expertise, event-driven systems experience, and familiarity with cloud and infrastructure tooling.

Own Ramp’s UK partnerships strategy and partner ecosystem as the senior commercial leader. Build executive relationships across accounting, investment, consulting, financial, and technology organizations; develop joint value propositions and commercial plans; generate pipeline and revenue through partner channels; represent Ramp at executive and industry forums; and establish operating cadences, scorecards, and cross-functional processes supporting UK and European market growth.

Design, build, evaluate, deploy, and operate production AI systems using LLMs, retrieval, agents, predictive ML, and model adaptation. Translate ambiguous business problems into reliable system behavior, establish evaluation and monitoring practices, troubleshoot complex failures, and improve AI-enabled product, engineering, and operational workflows. Collaborate across architecture, platform, security, data, quality, product, and domain teams while mentoring engineers and owning systems through continuous improvement.

Lead pharmacovigilance domain expertise for Veeva Falcon by guiding safety case intake, triage, validation, reconciliation, and expedited reporting workflows. Evaluate and calibrate AI-driven outputs, define complex testing scenarios, shape automation strategy, and translate early-adopter feedback into platform improvements. Serve as the bridge between PV operations, technical teams, and executive customers.

Leads internal tax technology initiatives from opportunity identification through implementation, adoption, testing, and benefits measurement. The role delivers AI and automation solutions, translates tax business needs for technical teams, manages stakeholders, supports user training and change adoption, and improves tax processes through digital transformation.

Lead ARIA’s Scaling Inference Lab, an AI hardware testbed building deployable rack-scale systems. Own programme delivery, operating model, governance, funding strategy, partnerships and long-term sustainability. Represent ARIA in governance forums, assess portfolio progress and risks, guide evidence-based decisions, and build relationships across academia, industry, government, funders and the compute ecosystem. The role requires technical breadth across AI infrastructure, strong programme leadership, senior stakeholder management, and regular UK travel.

Investigate and resolve customer complaints, clinical safety issues, and product risks across diagnostic products. Manage complaint lifecycles, assess regulatory reporting obligations, conduct root cause analysis, support CAPA and post-market surveillance, maintain hazard logs, and apply risk management standards. Use data analytics and AI tools for complaint trending and signal detection, present findings to stakeholders, and maintain audit-ready quality records while supporting audits, inspections, and continuous improvement.

Own and improve Clipboard’s internal operations tooling ecosystem by building workflows, integrations, and automations. Partner with Engineering and Operations leaders to define requirements, write technical specifications, prioritize initiatives, troubleshoot production issues, improve system reliability, and introduce AI-driven solutions that increase operational efficiency and scalability.

Manage multi-country payroll operations, compliance, audits, controls, vendor relationships, payroll analysis, and process improvements. Lead payroll system automation and scalability initiatives while ensuring accurate and timely processing across jurisdictions. Partner with finance, HR, treasury, and external providers, monitor regulatory changes, and mentor a global payroll team in a distributed technology environment.

Build production machine-learning features that transform raw AI usage data into customer-facing insights. Own classification, clustering, data pipelines, model deployment, evaluation frameworks, ground-truth datasets, error analysis, uncertainty quantification, and production reliability. Collaborate with customers, product, engineering, and security teams to shape insight experiences. Work primarily in Python across a polyglot Java, Spring Boot, and TypeScript stack deployed on AWS.

Design, develop, and maintain secure, high-quality production code and system architectures. Troubleshoot complex issues, analyze large datasets for improvements, ensure application resiliency and CI/CD practices, and contribute to engineering communities and inclusive team culture.

Build and deploy production AI agents and full-stack automations for insurance claims. Identify workflow bottlenecks by working directly with claims handlers, turn opportunities into shipped features, and own system reliability, correctness, and risk. Collaborate with AI engineers and operations while scaling real-world claims automation in an office-based London environment.

Design, architect, and implement production-grade agentic AI systems. Drive agentic product direction, identify opportunities, build prototypes into scalable production code, integrate with partners and platforms, and establish evaluation, observability, and regression-testing practices. Collaborate closely with Product and support customer and partner technical integrations while raising engineering standards across the applied-AI team.

Own enterprise customer relationships and drive measurable outcomes through platform adoption, AI enablement, change management, renewals, and expansion. The role guides customers in applying AI agents to GTM workflows, leads executive business reviews, monitors health and usage data, mitigates churn risk, identifies growth opportunities, and collaborates with Sales, Product, Marketing, RevOps, and Professional Services.

Lead the organization’s AI governance and assurance framework, embedding Responsible AI, risk management, compliance, and controls throughout the AI lifecycle. Assess generative and agentic AI systems for fairness, explainability, robustness, security, and regulatory alignment. Conduct risk assessments, DPIAs, supplier reviews, and assurance activities while monitoring emerging regulations. Partner with Product, Engineering, Risk, and Compliance teams; develop policies, training, standards, and governance practices that promote safe, transparent, and accountable AI adoption.

Design, build, deploy, and optimize production AI systems, including LLM integrations, RAG pipelines, embeddings, vector search, predictive models, and inference systems. Collaborate with product and engineering teams to translate business needs into scalable solutions for recommendations, risk scoring, forecasting, and workflow automation. Own data pipelines, monitoring, evaluation, security, privacy, documentation, and continuous model improvement across cloud environments.

Build and improve production AI systems including chatbots, document extraction pipelines, LLM workflows, and voice AI. Responsibilities span discovery, prototyping, Python development, deployment, evaluation, monitoring, and process automation. The role requires applying LLMs, retrieval systems, prompts, and agent workflows to simplify financial-services operations and autonomously complete customer account actions.

Build and improve production AI systems for fintech operations, including chatbots, document extraction pipelines, LLM workflows, and autonomous account actions. Responsibilities span opportunity discovery, rapid prototyping, prompt and retrieval optimization, deployment, evaluation, monitoring, and process automation. The role requires strong Python engineering, practical LLM experience, sound judgment in ambiguous situations, and the ability to communicate trade-offs and influence stakeholders.

Leads BNY’s enterprise AI Risk team and second-line oversight framework. Responsibilities include establishing AI risk principles, governance, controls, monitoring, assessment, issue management, and reporting. The role provides credible challenge to senior technology and business leaders, coordinates across Risk and Compliance functions, tracks evolving AI regulations and threats, and promotes responsible AI adoption while balancing innovation with enterprise risk requirements.

Leads enterprise-wide product strategy and transformation from the Corporate Strategy Office. Partners with product, engineering, design, and business leaders to scale AI-enabled product development, improve product operating models, coach leaders, and deliver customer and business outcomes. The role leads lighthouse initiatives, develops reusable tools and standards, establishes success metrics, and influences organizational change across a global software company.

Deliver and deploy ML and generative AI solutions across industries: design experiments, build production-scale models, identify business use cases, drive measurable value, consult with clients, and mentor team members.

PhysicsX invites candidates to join its talent community for future opportunities across research, data science, machine learning engineering, product, and delivery. The company develops AI-driven simulation software for engineering and manufacturing in advanced industries including aerospace, energy, semiconductors, materials, and automotive. Applications are reviewed for potential future roles, with no active vacancy or immediate progression guaranteed.