Top AI Jobs in London

Lead the AI function, define roadmap, and build production generative AI systems (voice, chat, document). Split time between hands-on engineering (~40%) and leading the AI team, establishing RAG/agentic workflows, evaluation, observability, and best practices. Collaborate with product and engineering to deliver reliable, user-focused AI features.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Join Verve as an Insight Associate, focusing on AI-powered market research. Learn to design research, interpret human behavior, and contribute to client projects using AI tools.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Develop and execute go-to-market strategy for AI products: define positioning and messaging, conduct market and competitive analysis, enable sales, coordinate launches, gather customer insights, and measure product performance.

Lead AI Developer to capture client requirements, translate workflows into technical designs, provide technical leadership and mentoring, and contribute hands-on development in .NET/C#/Azure/React. Implement generative AI features (LLMs, RAG, Agentic AI), oversee backlog refinement, sprint planning, code reviews, testing, and support Azure, Power Platform, and M365 cloud-first delivery.

Lead AI transformation programmes for local government using PRINCE2 Agile and Scrum. Coordinate technical teams, manage budgets, risks, governance, stakeholder engagement, reporting, documentation, and benefits realization while acting as primary client contact and steering group representative.

Design and deliver enterprise AI solutions using Microsoft cloud technologies. Develop .NET software, build prototypes, QA releases, debug across environments, produce technical documentation, and support engineering process improvements within agile teams.

Provide technical leadership and client-facing requirements capture; translate business processes into technical designs; hands-on development in C#/.NET, Azure and React; implement generative AI features (LLMs, RAG, Agentic AI); mentor developers; lead sprint planning, backlog refinement, code reviews and QA; support Azure, Power Platform and M365 cloud-first delivery.

Lead organisational change for AI implementations across public-sector clients. Design and deliver change strategies, stakeholder engagement, communications, training, readiness assessments, adoption tracking, benefits realisation, and support transition to business-as-usual alongside delivery teams.

Lead multiple concurrent AI transformation programmes for public sector clients, owning governance, benefits realisation, budgets, risk, stakeholder management, change and adoption, and ensuring responsible AI deployment across delivery teams.

Own product areas across an AI-driven neobank app (payments, savings, investing, insurance, travel). Define problems, roadmap, success metrics; work with engineering, design, data, operations, compliance and business teams. Launch features, monitor performance, iterate using data and user feedback. Balance speed, UX, business impact and compliance while driving execution.

Lead product direction and execution for major AI neobank app areas. Define roadmaps, priorities, metrics, and product standards across squads. Guide PMs, designers and engineers; balance strategy, UX, data, engineering and business outcomes. Work across insurance, payments, savings, investing and travel; translate regulatory and operational constraints into practical product decisions. Drive launch quality, post-launch learning, and continuous improvement while building a fast, measurement-driven product culture.

Partner with Product, Design, Engineering, and Data to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and translate analytics into product recommendations to shape AI product strategy.

Build and ship agent orchestration workflows, integrations, and shared infrastructure for AI-driven growth across paid media, lifecycle, organic content, CRO, incentives, and referrals. Implement monitoring, audit logging, creative generation pipelines, and reliable integrations so domain experts can tune and operate workflows independently.

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

Own end-to-end research and applied engineering to build multimodal models that fuse 3D dental scans and clinical text, build 3D/2D data pipelines, design and train deep learning models (CNNs, transformers, point/graph nets), define evaluation and metrics, and move prototypes into production with robust ML engineering.

Build and ship native Android features in Kotlin (Jetpack Compose). Integrate backend APIs, improve performance and reliability, collaborate with product and design, use analytics, and implement AI-assisted on-device experiences where valuable.

Build and ship native Android features using Kotlin and Jetpack Compose. Design clean user flows for financial services, integrate backend APIs, improve performance and stability, use analytics and production feedback, and implement AI-assisted on-device experiences where beneficial.

Lead design and implementation of high-performance agent runtimes, backend services, and self-hosted model/agent infrastructure. Build low-latency execution systems, real-time data pipelines, telemetry loops, CI/CD for zero-downtime deployments, and ensure fleet reliability on AWS/EKS.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.