Top AI Jobs in London

Build rapid prototypes and proof-of-concept applications using ML models, generative AI tools, API integrations, and cloud deployment pipelines to demonstrate emerging technology potential for betting and gaming.

Founding Executive Director to build and run an AI consultancy: define offerings and pricing, win clients through direct sales and networks, own P&L, represent the brand publicly, shape training and product-recommendation services, and scale a delivery engine while working closely with the COO.

Lead development and implementation of EDF's Responsible AI framework and controls. Review higher-risk AI use cases, translate regulation into practical guidance, monitor AI regulatory/industry developments, and embed responsible AI practices across teams through engagement, governance and knowledge sharing.

Own and prioritise the shared AI platform backlog, translate business demand into epics and user stories, coordinate delivery across engineers, suppliers and stakeholders, identify dependencies and risks, and shape requirements to deliver AI platform capabilities and tooling that create business value.

Build production-ready AI agents and automations using n8n and LLMs to integrate systems via APIs/webhooks, use SQL and code (Python/JavaScript) to prototype, deploy and iterate autonomous workflows that reduce manual work across Sales, Operations, Supply Chain, Finance and Product.

Hands-on Technical Product Manager building automations and AI tools to remove manual work across sales, operations, supply chain and finance. Map workflows, prioritise opportunities, build n8n workflows, connect APIs/databases, develop LLM-based agents, use SQL and Python/JavaScript for prototypes, test with users, and embed solutions into operations.

Lead EMEA AI/ML product marketing: adapt global messaging to regional needs, align sales and product teams, create outcome-led customer stories, develop field enablement and campaign plays, track KPIs and usage to drive adoption and report impact to leadership.

Design, build, and ship production-grade LLM-powered features (RAG, extraction, summarisation, agentic tooling). Build data and inference pipelines, integrate with back-ends and vector stores, ensure evaluation, monitoring, cost and performance optimisations, and champion testing and reliability alongside product and tech leads.

Serve as a senior ML technical advisor and pre-sales engineer, designing production-grade ML/GenAI solutions on Databricks. Build MVPs, guide MLOps/LLMOps architectures (including RAG and vector DBs), integrate cloud services, mentor teams, create technical content, and support platform adoption through customer engagements and presentations.

Lead function-aligned AI engineering to accelerate responsible AI adoption across Global Markets. Identify and prioritise high-value AI use cases, design and deliver generative AI, RAG and agentic workflows, ensure governance/compliance, integrate with enterprise platforms, embed controls and monitoring, deliver prototypes to production, and track benefits and adoption while coordinating with risk, legal, data and technology stakeholders.

Design, implement, and deploy stateful agentic LLM orchestration and RAG systems integrated with internal APIs, databases, and third-party tools. Collaborate in Agile teams to deliver reliable AI-automation solutions, contribute to code review and QA, improve system reliability, and communicate technical trade-offs with stakeholders.

Execute multi-region, multi-channel B2B demand generation programs for IFS Industrial AI. Manage account-based campaigns, audience segmentation, channel mix and budgets. Deliver and optimize ads, email, paid social, display, programmatic and video, use AI and martech to improve workflows, analyze performance and maintain reporting dashboards, and prepare materials for stakeholder and executive updates.

Develop and deploy AI-powered applications, agents, and integrations for HFT. Evaluate models and tooling, build agentic workflows, integrate AI with internal systems, and collaborate with research and trading teams to deliver production-grade AI solutions and best practices.

Build, customize, evaluate, and operate production generative AI solutions for customs workflows. Responsibilities include document understanding, classification, retrieval-augmented generation, structured extraction, tool calling, and multi-step agentic automation. The role owns pilots, evaluation frameworks, monitoring, cost and latency optimization, governance, privacy, security, and EU AI Act documentation. It also requires close collaboration with customs operations, IT, transformation teams, and technology partners.

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.

The AI Creative Designer creates high-quality, AI-generated visuals for marketing campaigns, collaborating with teams to optimize visual content for conversion rates.

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.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Manage and optimize end-to-end account reconciliations, intercompany eliminations, and chart-of-accounts governance. Identify discrepancies, perform variance analysis, and implement AI-driven automation and anomaly detection to reduce manual effort and accelerate close. Collaborate with cross-functional teams to resolve mismatches and improve financial data quality and reporting accuracy.

Design and implement AI/NLP solutions to automate insurance policy administration (issuance, endorsements, renewals). Translate policy language into models and rule engines, integrate with PAS, ensure regulatory compliance, monitor model performance, and support underwriting, operations, and exception handling.