Top AI Engineer Jobs in London

The Principal AI Platform Engineer leads AI architecture design and implementation, guiding teams to deliver secure AI solutions and mentoring engineers.

The AI Engineer will design and build LLM-powered agents and workflows for logistics optimization, focusing on fullstack development and infrastructure ownership.

Senior Azure AI Engineer working in an agile AI squad to design, develop and deploy Azure-based AI solutions — including assistants, copilots, retrieval/RAG systems, cloud-native applications, integrations, security/governance, testing and optimisation — collaborating with analysts, architects and delivery teams to deliver business outcomes.

Design and build practical AI tools, prototypes, and agentic workflows to improve productivity across business units. Create reusable patterns, APIs, and templates, drive AI adoption, optimize performance and costs, identify and mitigate AI risks, contribute to governance, and mentor colleagues while delivering enterprise-grade, regulated solutions.

The Senior/Staff Software Engineer, AI will develop AI-powered features, design backend services, and optimize workflows, ensuring integration and user experience. The role requires collaboration across teams to innovate and improve AI applications.

As a Staff Cloud Site Reliability Engineer, you will build and scale AI cloud platform reliability, define operational standards, and lead SRE initiatives.

The role involves building and scaling the reliability of AI cloud platforms, overseeing GPU compute infrastructure, incident response, and operational excellence while collaborating with various teams to enhance system efficiency and resilience.

Lead technical vision and execution for agentic AI across Funds Transfer Pricing and Financial Hedging. Architect and build scalable multi-agent systems, guide LLM, RAG, and vector-data strategies, enforce AI governance and XAI, mentor senior engineers, and align technical roadmap with business objectives.

Design and build production-scale agentic AI systems for funds transfer pricing and financial hedging. Architect multi-agent workflows, RAG and embedding pipelines, integrate LLMs and predictive models, optimize for low-latency/high-throughput, and provide technical leadership and mentorship across teams.

Design and implement secure, production-ready AI platforms and solutions; move proofs-of-concept to large-scale production; define governance, observability, security and operational standards; support technical discovery, mentor engineers, and develop reusable patterns and accelerators across cloud and data ecosystems.

Build and own core AI developer tooling: extend the companys AI coding agent, enable spec/testing/reliability features, ensure secure data access, and help teams adopt AI-enhanced SDLC practices to measure and improve developer productivity.

Design, prototype, and deliver applied ML and agentic systems using generative AI and multi-source scientific data to improve clinical trial design. Integrate historical trials and real‑world data into production systems, ensure evaluation and regulatory-appropriate guardrails, deploy cloud-based services, and communicate results to technical and non-technical stakeholders while contributing to engineering and data science best practices.

Design, prototype, and ship applied ML/AI systems that integrate multi-source clinical and scientific data to improve clinical trial design. Build generative-AI and agentic systems, production-grade Python services (containers, APIs, CI/CD), and evaluation/guardrails for regulated environments while communicating findings to technical and non-technical stakeholders.

Lead architecture and build an automation and AI-native toolchain (fusing traditional automation with agentic workflows), mentor teams, drive cross-department adoption, standardize practices, and evolve IT operations toward infrastructure-as-code and platform-first models.

Hands-on Staff Engineer who designs, builds, and deploys AI agents and LLM-based automation to redesign and automate the deal approval process. Integrate solutions with CRM, ERP, and finance data sources, own delivery end-to-end from prototype to production, and drive AI adoption and governance within Commercial Finance to reduce cycle time and manual work.

Lead modernization of the digital workplace across Microsoft 365 E5 and endpoint ecosystems. Design and operate Entra ID, Intune, Defender, Purview, Teams, SharePoint and device management. Build automation, IaC, Power Platform solutions, Power BI reporting and Copilot Studio agents. Drive Copilot adoption, ensure AI governance and compliance, provide third-line support, root-cause analysis and technical leadership to improve user experience and operational consistency.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.

Design and build enterprise AI platform capabilities on Azure focusing on agentic AI. Create standardized templates, runtimes, and secure model access patterns. Implement LLMOps, observability, CI/CD, and governance for safe, reliable, cost‑aware production AI. Partner with product, cloud infrastructure and security teams to enable scalable, auditable agent deployments and operational readiness.

The Applied AI Engineer will design and build LLM-driven automation products, collaborate on AI capabilities, and improve AI performance for wealth management solutions at Nevis.

The software engineer will develop AI tools for sustainability reporting, collaborating on product strategy and ensuring code quality while leveraging LLMs.

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

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.

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.

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.