Top AI Jobs in London

As an AI Engineer, you will build AI agents for preventive health, focusing on reasoning, clinical workflows, and ensuring reliable production systems.

Own the full enterprise GTM lifecycle: prospecting, discovery, demos, pilots, negotiations, closing, and expansion. Map creative workflows, run product demos and pilots with success metrics, manage complex multi-stakeholder deals, build repeatable GTM processes, and feed customer insights back to Product and Leadership to influence roadmap and positioning.

The Machine Learning Engineer will develop AI systems to enhance insurance claims workflows, deploy scalable ML solutions, and collaborate with insurance specialists.

The AI Engineer will focus on the lifecycle of production-grade AI, managing model training, fine-tuning, inference optimization, and production engineering, while collaborating with stakeholders to implement business requirements.

Lead AND Digital's Data & AI practice: define go-to-market strategy, advise C‑suite, design value-driven Data & AI solutions, grow revenue and partnerships, mentor teams, and ensure delivery excellence.

Entry-level role for recent graduates to research and support Data & AI innovation. Responsibilities include trend research, using GenAI tools (e.g., Claude), developing client-facing presentations, collaborating with senior experts, and contributing to new Data and AI offerings.

The Principal Engineer will lead the design and deployment of AI systems, develop GenAI features, and integrate LLMs, requiring deep ML knowledge and software engineering skills.

Define multilingual AI quality frameworks, design and run evaluations, curate and annotate datasets, analyze results, and partner with product, engineering, and research to improve AI experiences across languages and markets.

Lead technical pre-sales for AI and data opportunities across the UK public sector. Shape solution strategies, conduct client workshops, lead bids and procurements, advise on responsible AI adoption, and develop differentiated AI propositions. Provide expertise in generative and agentic AI, machine learning, LLM solutions, data foundations, governance, evaluation, and production adoption. Build senior stakeholder relationships, support demonstrations and proofs of concept, mentor colleagues, and ensure proposed solutions are practical, secure, scalable, ethical, and deliverable.

The AI Research Engineer will develop AI models to enhance product autonomy, analyze extensive data, and deliver impactful solutions, driving innovative product features.

Architect and build production AI-powered HR systems using agentic workflows, multi-agent systems, APIs, and workflow automation. Integrate Workday and other HR tools, implement governance, audit logging, privacy controls, and human approval gates, and ensure reliable payroll-adjacent workflows. Document solutions, train People team users, drive adoption, and measure automation impact. The role requires strong software engineering, AI application development, HR technology, Workday, data privacy, and workflow automation experience.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

As a Product Manager, you will lead AI-first pods, drive product roadmaps, prioritize customer needs, and collaborate effectively across teams to deliver AI-powered solutions.

Build internal AI enablement tools, workflows, and guardrails to help engineers use coding agents safely and productively. Evaluate models and agents, partner with teams to apply AI, track adoption and impact, and provide documentation, training, and advocacy across the engineering organization.

Lead global R&D to define technical strategy and roadmaps, prioritize AI-driven modernization, manage engineering and test teams, oversee AI/ML model validation in regulated clinical environments, own R&D budgeting and vendor partnerships, ensure regulatory compliance, and drive operational excellence and ethical AI adoption to improve clinical trial outcomes.

Design, build, and deploy Generative AI/LLM applications (chatbots, RAG, document intelligence) on GCP using Vertex AI, BigQuery, and containerised services. Work with data teams, implement CI/CD, monitoring, vector databases, and ensure Responsible AI, GDPR, and regulatory compliance while engaging stakeholders.

Design and own end-to-end agentic AI workflows on the Microsoft AI stack, focusing on agent orchestration, human-in-the-loop controls, cost optimization, secure enterprise integrations, governance, and phased multi-country deployments. Define reference architectures, optimize model routing and token budgets, and ensure compliance and auditability while evaluating emerging agent frameworks.

Deliver an AI-enabled DevOps and automation capability: design scalable CI/CD patterns, integrate Azure AI Foundry and AI services into pipelines, implement IaC and operational tooling, improve SDLC with AI, and provide documentation and structured knowledge transfer to TMHCC engineering teams.

Provide legal counsel on AI and data protection across customer, partner and supplier contracts; lead EU AI Act readiness and ISO 42001 alignment; advise on IP, data transfers, DSRs, audits, incidents and regulator engagement; draft playbooks, run trainings, and support multilingual contracting (French/Spanish) in a fast-growing AI/SaaS environment.

Lead design and delivery of AI-enabled transformation across global accounting: consolidation, R2R, tax, and revenue recognition. Redesign processes to be AI-first, embed AI agents and automation, ensure controls and audit compliance, strengthen validation and monitoring, build team capability, drive adoption through training and standardization, and align with broader finance transformation programs.

Design, build, and maintain automated UI, API, and end-to-end test frameworks integrated with CI/CD. Define risk-based testing strategies, transition manual regression to intelligent automation, validate AI-generated code and LLM outputs, collaborate to improve testability and production quality, and champion AI-native testing practices across engineering teams.

Lead EMEA go-to-market for Physical AI by sourcing executive-level pipeline, accelerating top accounts, building EMEA-specific playbooks, enabling regional sales, and converting lighthouse wins into sustained cloud consumption across robotics, autonomy, simulation, and related AI-native industries.

Lead quality engineering across product teams with an automation-first approach: build test frameworks, CI tooling, AI-assisted testing agents, performance benchmarks, reliable E2E suites, reduce flakiness, drive shift-left quality, and close the loop with observability to improve SLOs and reduce incidents.

Lead quality engineering across product teams with an automation-first approach: build test frameworks, AI-assisted testing agents, CI tooling, SLOs, performance benchmarks, reduce flakiness, improve test data and environments, and partner with product and engineering to prevent defects and close the loop on production issues.

Full-stack AI/ML Software Engineer to build HealthTech SaaS microservices, implement and integrate ML solutions, write high-quality code, author documentation, perform code reviews, and act as an SME on services and systems.