Top AI Jobs in London

Centralise and clean People data in Elmo, build recurring reports and at least one dashboard, and create automated workflows (using Elmo API, Zapier/Make or similar) to flag missing documents and trigger scheduled reports. Collaborate with managers to resolve data gaps and hand over documentation for ongoing maintenance.

Build and operate production-grade AI tools, agents, and workflows to accelerate product development. Partner with teams to embed agentic workflows, define guardrails and governance, measure adoption and model performance, and deliver scalable, observable, and secure AI-native capabilities.

Manage business relationships with GSIs in EMEA, focusing on AI-enabled services and program development partnering with NVIDIA solution architects. Drive sales strategies, track progress, and foster collaboration among teams.

The AI Enablement Intern at Providence will support AI solutions for investment teams, build workflows, drive adoption, and improve decision-making across portfolio companies.

As an AI Creative Producer, you'll develop and scale AI-generated content across various platforms, collaborate with teams, and enhance production workflows.

The Director of Business Development will drive revenue through creating strategies for new business, managing client relationships, and overseeing sales processes while ensuring high service standards.

As a Principal Forward Deployed Engineer, you'll deploy JetBrains Central in enterprise environments, implement AI governance, mentor teams, and advise customers on AI-native development best practices.

The AI Operations Specialist at Moss will enhance AI adoption by collaborating with teams to implement AI solutions, automate workflows, and drive operational improvements using various AI tools.

The Senior AI Engineer will design and implement AI systems, focusing on production-grade solutions using LLMs and agentic systems to meet business needs.

As the Search & AI Retrieval Lead, you will architect and implement scalable search systems, design knowledge graphs, and ensure effective data retrieval on GCP while leading technical initiatives.

The role involves leading programming teams in biometry, developing technical solutions, ensuring regulatory compliance, and driving digital transformation in clinical data analysis.

The Managing Consultant will develop AI-driven transformation strategies for financial services, overseeing client engagements and business development while leveraging industry connections and expertise.

The Sr Director Analyst will create high-quality research content on analytics and AI, providing strategic insights and advice to clients while engaging them through presentations and discussions. This role involves mentorship, innovation discussions, and requires deep industry knowledge and executive presence.

Lead Swift's AI and analytics strategy by scanning trends, identifying high-value opportunities, driving experiments to validated outcomes, building a prioritized portfolio, developing a team, and representing Swift externally to accelerate enterprise AI adoption.

Build and deploy client-facing agentic cybersecurity solutions: compose red-team/blue-team workflows, configure harnesses for cloud defense, vulnerability scanning and pen-testing, design context engineering and multi-agent orchestration, turn prototypes into production services, iterate with client feedback, and collaborate with pentesters and SWE-Cyber for accuracy and platform support.

Design and implement scalable graph-based search services and APIs. Build backend systems using Java and Spring Boot, integrate AWS services and databases, ensure observability with New Relic and Coralogix, and champion clean code and engineering best practices while collaborating across product and engineering teams.

Lead FP&A analytics and commercial governance to protect profitability, reduce losses, and strengthen controls. Drive end-to-end AI use cases—from concept to production—using Microsoft Fabric and Azure tools to improve forecasting, scenario analysis, margin visibility, and decision support. Partner with FP&A and stakeholders to challenge assumptions, surface risks, and embed AI-driven FP&A workflows across the business.

Lead adoption and build AI agents and agentic workflows for social media operations using OLIVER's proprietary platform. Drive client discovery, pitch agent solutions, design multi-agent architectures, implement prompt engineering and RAG, create training programs, and feed product insights to tech teams to scale AI-enhanced social processes and measure ROI.

The role involves leading the identification, design, and delivery of AI solutions, managing transformation programs, and ensuring measurable outcomes and team capabilities.

Lead and deliver AI-native customer experiences by designing frontend-to-orchestration architectures, prototyping LLM-driven interfaces, mentoring engineers, running POCs, advising clients on AI transformation, and operationalizing RAG, vector storage, agentic systems, and AI observability.

Early-career AI engineer who ships production features rapidly using AI-augmented workflows. Own end-to-end features, prototype and evaluate LLM-driven solutions, collaborate with PMs and customer teams, pair with senior engineers, and maintain clear, tested, maintainable code while measuring impact.

Architect and implement scalable generative-AI solutions for enterprise document workflows. Own end-to-end delivery from customer discovery through deployment, build advanced extraction and automation systems, ensure privacy/security by design, mentor engineers, and collaborate with product and commercial teams to drive customer adoption and measurable business value.

Enable and govern practical AI usage across software engineering teams: evaluate and rollout AI-assisted dev tools, embed AI into SDLC and CI/CD workflows, create guidance and playbooks, partner with stakeholders (Security, Model Risk) to ensure safe, compliant adoption, and build lightweight tooling to improve developer productivity and quality.

Lead adoption of AI-assisted software engineering across teams: evaluate and introduce AI tooling, embed AI into the SDLC and CI/CD, define success metrics (DORA/flow), partner with Security and Model Risk to govern usage, build/extend AI capabilities, and coach engineers to improve productivity, quality, and developer experience.

Lead EMEA Applied AI strategy and build an organisational capability, delivering production AI systems (LLM co-pilots, agentic workflows, ML inference) integrated into banking/trading infrastructure. Own platform (pipelines, feature stores, model registry), governance, and stakeholder management to scale AI adoption, ensure regulatory compliance, and demonstrate measurable business impact.