Top AI Engineer Jobs in London

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.

Client-facing role designing and delivering modern data platforms, architectures, and pipelines. Lead data strategy, governance, and engineering to enable analytics, ML/AI and GenAI use cases. Provide architectural leadership across programmes, support business development, thought leadership, and mentor junior staff while implementing cloud-native data solutions and governance frameworks.

Lead Mark43's AI-enabled engineering initiative by designing and orchestrating multi-agent systems that plan, write, review, test, and deploy software. Write prompts, scaffolding, and tool integrations (VCS, test runners, internal APIs), evaluate agent performance, prototype LLM-based workflows, collaborate cross-functionally, mentor teammates, and contribute to on-call rotations.

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.

The Lead Engineer will develop secure, scalable backend and frontend systems, establish coding standards, mentor engineers, and lead AI integration efforts while ensuring quality delivery.

Design, build, and own agentic LLM workflows and backend services powering an AI copilot for blockchain investigations. Lead architecture, tool integrations, retrieval pipelines, evaluations, and mentoring. Ship production-ready LLM features with strong testing, observability, and reliability practices while collaborating across product, web, data, and DevOps teams.

Build production AI-powered tools, data pipelines, and internal full-stack applications that automate incident investigation, surface anomalies, and enable AI agents to interact safely with platform systems. Work across Python, TypeScript, Go and SQL, deploy on Docker/Kubernetes/AWS, and collaborate with TechOps, Connectivity, and Data teams to own, monitor, and improve shipped systems.

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.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Lead technical pre-sales engagements for Databricks, demoing Spark and data/AI solutions, building architectures, authoring reference materials, and collaborating with account teams to design and deliver customised proofs-of-concept and workshops.

As an AI/ML Software Engineer, you'll develop systems that transform audio and video data into structured insights, focusing on prompt engineering, agent systems, and model optimization.

The Senior AI & Machine Learning Engineer will design and develop AI systems, implement ML algorithms, and collaborate on applications in AI and Finance.

Design and build core backend systems for an agent platform: orchestration, durable execution, memory, tool integration, observability, and evaluation. Create scalable services/APIs, develop retrieval and long-term memory solutions, monitor and improve agent behavior, and help drive technical direction and team growth for AI-driven collaboration features.

Lead identification, design, build, and adoption of AI-driven automations and internal tooling across GTM, product, finance, ops, and engineering. Partner with functional leads, prototype code-first workflows and low-code automations, work closely with engineers to improve developer productivity, and maintain internal knowledge systems for compounding value.

Drive end-to-end research and engineering on vision-language and multimodal models: data curation, training pipelines, post-training workflows (fine-tuning, distillation, RLHF), efficiency and deployment optimizations, distributed GPU training, evaluation frameworks, and publishing research.

The Senior Software Engineer will enhance software development using AI tools throughout the lifecycle, ensuring quality and maintainability while managing a complex codebase.

Design and build scalable AI-powered backend services and APIs in Golang for cloud-native, microservices-based platforms. Develop developer tools, ensure observability, CI/CD, and performance, solve distributed-systems challenges, and mentor engineering teams to deliver production-ready solutions.

Deploy and maintain AI enablement infrastructure (MCP servers, AI agents, hosting) and cloud environments on AWS/GCP. Build IaC and CI/CD pipelines, monitor performance/cost, support containerized services, manage API keys and permissions, document runbooks, and collaborate with Security, Engineering, and stakeholders to improve cloud governance and AI tooling adoption.

Build and operate cloud-based infrastructure and tooling to host, deploy, monitor and govern ML and GenAI models and AI agents. Standardise AI development workflows, integrate third-party AI tools, embed Responsible AI practices, and partner with Product, Data Science, Security and Legal to scale AI across the business.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

Join Tether as an AI Research Engineer focusing on model compression and quantization for AI systems. You'll innovate in reducing model footprint for multimodal architectures while applying advanced compression techniques, ensuring high performance on edge devices.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will optimize model serving and inference architectures, focusing on high-performance, resource-efficient solutions for AI systems across various deployment environments.