Top AI Engineer Jobs in London

Design, build, and maintain scalable, secure cloud infrastructure across AWS and Azure using Terraform, Docker, and CI/CD. Manage production environments, monitoring, security, IAM, secrets management, automation, cost optimization, and collaborate with dev/DevOps teams to ensure high availability and reliability.

Lead design, development, and delivery of production AI systems and orchestration for insurance workflow automation. Architect inference pipelines, routing, caching, and fallback strategies. Drive latency, reliability, cost, and scalability decisions; implement observability and incident response; mentor engineers and guide cross-functional teams to ship production-grade AI features.

Lead design, architecture, and implementation of production agentic systems. Collaborate with Product to scope opportunities, build prototypes to production, integrate with partners/customers, and ensure robust evaluation and observability for deployed agents.

Define and build Fuse's inference-serving architecture and stack, including request routing, batching, scheduling, and autoscaling for latency-sensitive workloads. Own model-level optimizations (quantisation, distillation, speculative decoding), translate performance SLAs into capacity plans, and integrate low-level GPU/CUDA performance work with serving infrastructure. Set benchmarks, tooling, and act as technical owner of inference performance and reliability.

The Principal AI Engineer will build and improve the core AI tutoring system, design evaluation methods, and collaborate on pedagogical goals, driving innovation in learner experiences.

Build and evolve an AI tutoring system, run evaluations, design software, collaborate with teams, and ensure quality through testing and refinement.

Responsible for building and evolving an AI tutoring system, running experiments, collaborating with teams, and ensuring quality in model behaviors.

As a Principal AI Engineer, you'll build and evolve AI tutoring systems, run experiments for learning effectiveness, design software, collaborate across teams, and prototype AI interactions.

Develop scalable cloud infrastructure and robust software solutions for AI/ML, collaborating with engineers and scientists to enhance scientific research.

Uncountable seeks a Platform Engineer focused on Generative AI to develop AI-powered tools for scientific R&D, requiring fullstack development skills and a passion for advancing user needs.

Responsible for leading quality assurance across underwriting lines, improving testing strategies with automation, and mentoring QA teams to enhance delivery quality and efficiency.

As an AI Software Developer, you'll create AI-driven solutions, collaborate with teams, gather requirements, and manage product deliverables in a financial context.

Serve as a senior ML technical advisor and pre-sales engineer, designing production-grade ML/GenAI solutions on Databricks. Build MVPs, guide MLOps/LLMOps architectures (including RAG and vector DBs), integrate cloud services, mentor teams, create technical content, and support platform adoption through customer engagements and presentations.

As a Staff Software Engineer at The Trade Desk, you will design, build, and deliver data-focused products, contribute to solution design, and mentor other engineers. You will work on large-scale distributed systems and collaborate in a diverse team environment to solve complex problems.

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

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

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.

Lead adoption of AI-assisted software engineering across teams: evaluate and introduce AI tooling, embed AI into the SDLC and CI/CD, define success metrics (DORA/flow), partner with Security and Model Risk to govern usage, build/extend AI capabilities, and coach engineers to improve productivity, quality, and developer experience.

Enable and govern practical AI usage across software engineering teams: evaluate and rollout AI-assisted dev tools, embed AI into SDLC and CI/CD workflows, create guidance and playbooks, partner with stakeholders (Security, Model Risk) to ensure safe, compliant adoption, and build lightweight tooling to improve developer productivity and quality.

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.

The AI Deployment Engineer will guide enterprise customers in deploying Generative AI applications, ensuring business value realization and managing project lifecycles from prototyping to production.

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.

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 role involves building AI features alongside product teams, enhancing AI competency through workshops, and maintaining AI architecture post-launch.