Top AI Engineer Jobs in London

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.

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.

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

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.

Build and ship agentic LLM systems for Treasury: cash monitoring, funding execution, narrative generation, and discovery-to-deployment pipelines. Create reusable agent infrastructure, governance, evaluation, and partner with engineering to ensure data accuracy while embedding with operators to scope and iterate solutions.

The Applied AI Engineer will onboard customers, deploy AI applications, and collaborate with teams to resolve technical challenges and drive technological transformation.

Join Lendable as a Senior AI Engineer to build AI systems, improve developer experience, and deliver impactful solutions using machine learning and AI tooling.

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.

Lead design and delivery of production LLM-powered agent systems and firm-wide agent standards. Build reference agent apps, connect agents to internal data/APIs, establish security and governance for AI, adopt deployment and observability infrastructure, and partner with stakeholders to identify high-value use cases.

Build and deploy production agent applications integrated with internal databases, APIs, and data sources. Create reusable, secure starter templates and partner with business teams to identify, prioritize, and iterate on agent use cases while following and improving firm standards for deployment, security, and observability.

Lead design, build and deployment of production AI workflows embedded with business teams using Microsoft Copilot Studio, Azure AI Foundry and Microsoft Fabric. Own API integrations, CI/CD, MLOps/AIOps, testing, documentation and handover. Provide senior technical leadership, set standards, rapidly prototype in sandbox environments and ensure reliable, secure, production-ready AI solutions connected to enterprise systems (SAP, Salesforce, data lake).

Drive architecture and technical strategy for AI agents in the Fintech line of business, collaborate on product delivery, mentor team members, and enhance software development practices.

Design, build, and deploy production-ready AI algorithms and high-scale evaluation pipelines focused on LLMs and patent generation. Implement advanced prompt engineering, translate research into product across the stack (Python, Postgres, React/TypeScript), and benchmark models to drive R&D decisions.

As an AI Engineer, you will architect, develop, and deploy scalable AI applications, collaborating with teams to implement AI solutions that drive enterprise productivity and innovation.

Join Abound's one-year Graduate Scheme on the AI Engineering route to build production-grade Generative AI and agent solutions. Work cross-functionally to design prompts, integrate LLMs with tools, APIs and databases, write scalable Python code, and evaluate AI systems. You'll ship code from week one, receive mentorship, and may convert to a permanent role based on performance.

Build and maintain backend services and infrastructure for AI-powered agents, implement prompt engineering and RAG workflows, support React front-end integrations, work with MySQL and vector databases, monitor and optimise production LLM systems.

Design, implement, deploy, and operate production-grade AI systems powered by LLMs. Build LLM applications, orchestration, RAG retrieval systems, integration layers, scalable infrastructure, observability, and CI/CD. Collaborate with product and engineering teams and iterate from prototype to production.

Lead a new AI Ops team (start with two engineers) to build, ship, and scale automations and agentic tooling across internal operations; prioritize requests, maintain AI tooling health, integrate AI into products, train teams, and demonstrate measurable value.

The AI Enablement Engineer leads programs for engineering teams to integrate AI, teaches best practices, and develops scalable learning models.

Design, build, and ship production LLM-based systems and agentic workflows. Develop core learning libraries, human-in-the-loop feedback loops, confidence scoring, and scalable agent orchestration. Work with enterprise customers to prototype, deploy, and generalize solutions back into the platform.

Build and deploy AI systems for investment insights, focusing on ML and LLM-driven solutions, while ensuring high-quality data flow and outcomes.

Design, build, and deploy AI-powered solutions (LLMs, RAG, agents) to improve productivity and operational efficiency. Partner with business teams to define use cases, integrate AI with enterprise systems, create production-ready prototypes, and ensure solutions meet governance, security, and responsible AI standards.

Design, build, and integrate generative AI and AI-agent solutions for customer operations, automation, and lending workflows. Prototype quickly, implement Python-based systems, connect LLMs to APIs and databases, design prompts and evaluation processes, and improve production reliability with testing, monitoring, and fallback handling.

Lead design, development, and production deployment of generative AI solutions on Azure and Databricks. Build LLM-driven applications (RAG, agentic workflows) using LangGraph/LangChain, implement backend APIs with FastAPI, containerize with Docker, deploy on Kubernetes/AKS, architect search/retrieval with Azure AI Search and MongoDB, and mentor engineers while driving architecture, monitoring, and responsible AI practices.

Design, build, and operate internal AI/ML platforms and MLOps pipelines. Drive Infrastructure-as-Code adoption with Terraform, lead technical design for agent/multi-agent systems, set engineering standards, mentor engineers, and align AI solutions with business goals.