Top AI Engineer Jobs in London

The Senior Data Scientist will unify technical vision, develop and deploy ML models, and enhance AI strategy across teams. Responsibilities include collaboration, ownership of projects, and maximizing value through AI and data science techniques.

Build secure, scalable AI/ML platforms and applications to productionize research. Collaborate with AI researchers and data scientists, implement CI/CD and IaC, support experimentation environments, automate remediation, mentor junior engineers, and promote secure, responsible AI development practices.

Design, build, and maintain AI agents and automated workflows for marketing. Identify automation opportunities, deploy and monitor agents, integrate with marketing tools, and report efficiency and business impact. Ensure data quality, privacy, and documentation, and enable marketing teams to adopt automations.

As an Applied AI Engineer, you will build AI applications for public sector clients, create and evaluate datasets, and collaborate with teams to address challenges using AI solutions.

Design, build, test, and deploy scalable AI services integrated with operational systems. Develop Python services, REST APIs, ETL/data pipelines, IaC, and monitoring; collaborate with AI specialists and operations stakeholders to improve workflows, controls, and production readiness in Markets Operations.

Forward Deployed AI Engineers develop Gen AI solutions by building workflows, implementing strategies, and solving real-world problems at scale with clients.

Design, build, and productionize autonomous and assistive multi-agent AI systems. Implement RAG and semantic search with vector databases, integrate tools/APIs, apply prompt engineering and guardrails, orchestrate microservices workflows, instrument agents for metrics, run evaluations/A-B tests, and mentor teammates.

Design, build, and deploy production-grade generative AI and multi-agent systems for financial services. Architect scalable MLOps pipelines, optimize LLMs and multi-modal models, implement RAG and prompt-engineered agent workflows, and collaborate with clients and cross-functional teams to accelerate GenAI adoption and deliver measurable business impact.

As a Lead AI Engineer, you'll architect and deploy generative AI systems, optimize large models, and lead teams in AI engineering across financial services.

Design, develop, and deploy enterprise-grade Azure AI solutions including LLM-powered chatbots, vector-search integrations, and autonomous agents. Collaborate with cross-functional teams to build secure, governed, scalable AI platforms, optimise data flows, and support AI adoption and engineering best practices.

The Senior AI Engineer will design and deliver agentic AI systems, manage multi-agent workflows, make architectural decisions, and oversee production quality in a fast-paced environment.

Design, build, and operate low-latency multi-agent LLM systems. Ensure high-quality real-time communication and optimize prompt programming and retrieval processes.

As an ABAP Developer, you will leverage your SAP expertise to enhance AI frameworks, contribute to full-stack development, and mentor peers in SAP contexts at Conduct.

As an AI Engineer, you'll develop LLM-based user-centric products, creating frameworks, optimizing systems, and collaborating with cross-functional teams in a startup environment.

Design, build, and operate production-grade agentic AI systems on Amazon Bedrock. Implement multi-agent orchestration, MCP servers, RAG pipelines, vector stores, and integrations. Use AWS serverless (Lambda, DynamoDB, S3), instrument observability (CloudWatch/tracing), and collaborate with client teams to deliver scalable AI agent solutions.

Partner with stakeholders to discover AI use cases, configure enterprise GenAI tools, build and maintain high-quality prompts, contexts, and RAG/document-intelligence pipelines, evaluate and improve AI outputs, create reusable playbooks/templates, and enable teams on governed, production-ready AI workflows.

Contract AI Engineer to design, build and deploy LLM-driven automations and integrations across Salesforce, Zendesk, HubSpot, Zuora and Thrive LMS. Implement RAG pipelines, vector DBs, event-triggered workflows, and API/webhook integrations using n8n/Make/Zapier and direct LLM APIs. Ensure secure, GDPR-compliant production operations with logging, monitoring, and clear handover documentation.

Build, deploy, and maintain LLM-powered applications and coding-agent workflows for enterprise clients. Prototype with agent platforms, integrate to client systems securely (GDPR), implement monitoring, CI/CD, and token tracking, create reusable AI assets, and coach teams to production-grade solutions.

Build and embed AI/LLM capabilities into FP&A solution assets: design prompts, AI workflows and prototypes; implement features for planning, forecasting, reporting and insights; validate AI outputs; document prompting logic and enablement materials; collaborate with product, architecture, and FP&A experts.

Lead pre-sales technical evaluations for LogicMonitor's Edwin AI: run discovery, demos, and proof-of-value; configure use cases, prompts, dashboards; train sales and customer-facing teams; translate customer challenges into Edwin AI solutions; document requirements and outcomes in Salesforce; drive successful evaluations and resolve technical obstacles.

Design, build, evaluate and operate advanced AI agents and applications on Azure. Implement RAG and vector-search solutions, integrate LiteLLM/MCP gateways, instrument telemetry into Langfuse and Datadog, and experiment with contemporary large models and agent harnesses to optimise quality, cost, and safety.

Lead design, build, and productionalisation of AI and intelligent automation across Microsoft low-code and pro-code stacks. Deliver Copilot/agent experiences, Power Platform solutions, and Azure AI Foundry models. Own integrations, MLOps/LLMOps, governance, Responsible AI, technical leadership, mentoring, and driving enterprise adoption of agentic solutions.

Build and scale production Generative AI/LLM platforms and services (RAG, agents, vector DBs), create knowledge retrieval and embedding pipelines for heterogeneous docs, implement observability/evals and governance, act as technical Centre of Excellence and enable safe, cost-effective AI usage across the business.

Lead design and delivery of scalable GenAI platforms and services: build RAG pipelines and agents, model/version management, monitoring and drift mitigation, cloud-native microservices and deployment, testing and guardrails to reduce hallucinations, ensure compliance, and mentor engineering teams.

Design, build, test, and maintain scalable full-stack web applications and integrations using Java, Python, and React. Implement AI-enabled features, event-driven data pipelines, and marketing integrations (CDP/CRM/CMS). Improve code quality via AI-assisted tools, CI/CD, observability, and security/compliance best practices while collaborating with product, marketing, and partners.