Top AI Engineer Jobs in London

The role focuses on engaging the developer community to drive adoption of Tether's technology through advocacy, technical content creation, community building, and collaboration with cross-functional teams. Responsibilities also include presenting at events and fostering feedback integration into product development.

As an AI Inference Engineer, you will optimize C++ systems for AI model inference on edge devices, collaborating with researchers to deploy and enhance models while ensuring runtime stability and performance.

As a software engineer in generative AI, you will design and develop secure applications, optimize APIs, and collaborate with cross-functional teams to enhance AI solutions.

As an AI Deployment Engineer, you'll partner with enterprise customers to implement AI solutions, architect applications using WRITER's platform, and ensure customer success through effective integration and support.

Evaluate AI-generated coding interactions, assess quality of engineering judgments, provide feedback, and define standards for AI coding agents. Comfortable making subjective judgments about engineering quality.

The Senior Full-Stack Engineer will build and maintain high-performance frontends, manage backend data systems, and ensure optimal data transfer/storage while working in Physical AI and robotics environments.

The Staff Product Engineer will build AI-powered products, collaborate with ML scientists, contribute to software development phases, and mentor other engineers.

The AI Infrastructure Engineer will build and optimize training pipelines and inference services for AI models, enhance GPU-level performance, and mentor team members.

As an AI Research Engineer at Helsing, you'll architect and enhance deep learning frameworks, optimize infrastructure for distributed training, and develop strategies for large datasets while collaborating with a cross-functional team.

You will design, train, and deploy agents in multi-agent environments, enhancing our reinforcement learning capabilities and integrating cutting-edge AI into production systems.

The role involves developing, fine-tuning, and optimizing large language models, building ML infrastructure, and implementing CI/CD practices for AI/ML systems.

The Principal Software Engineer will lead the design of GenAI solutions, collaborating with teams to enhance AI capabilities in drug discovery and clinical development.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Build and deploy production Generative AI solutions using LLMs, RAG, embeddings, and agents. Implement retrieval pipelines, collaborate cross-functionally, write maintainable code, run POCs for new AI tech, and improve AI development practices and tooling.

Customer-facing AI engineer delivering and productionizing GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, present at conferences, and collaborate with product and engineering to shape roadmap and influence priorities.

Design, develop, and deploy intelligent AI agents, optimize large language models, enable collaboration in customer interactions, and maintain backend systems.

Own and evolve Xantura's text analytics platform: build NLP pipelines, LLM-based extraction and RAG, manage embedding models and vector DBs, maintain classical NLP components, and design agentic AI while ensuring explainability and Responsible AI for sensitive health and social care data.

Design, build, and maintain scalable, secure full-stack applications using Java and Angular. Lead integration and deployment of agentic AI, implement microservices and event-driven architectures, collaborate with data scientists, and promote CI/CD, testing, security, and best practices. Mentor developers and support the full software lifecycle in a hybrid enterprise environment.

Design and industrialise production-grade LLM systems and reusable AI platforms on Azure. Build RAG pipelines, agentic workflows, orchestration, evaluation and observability frameworks, and safety guardrails. Optimize for performance, cost and reliability while enabling horizontal reuse across squads and embedding AI risk controls.

Build and operate the AI Portal SaaS platform: develop services, APIs, integrations and UIs; improve reliability and observability; triage and resolve production incidents; participate in on-call rotation; deliver end-to-end product features for internal and partner customers.

Develop and benchmark 3D computer vision and localisation systems (SLAM, visual odometry, scene matching, geo-registration, 3D reconstruction). Extend geometric deep learning and feature-matching methods, design experiments and benchmarks, adapt models to hardware constraints, and collaborate with research, engineering, and product teams to deploy robust AI solutions.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Design and deploy production-grade LLM extraction agents and multimodal pipelines to parse unstructured pathology and genomics records, implement interoperable APIs (REST, HL7/FHIR), ensure data mapping and de-identification, build confidence scoring and human-review loops, and maintain compliance, audit logging, and data-quality observability for clinical use.

Own product areas end-to-end, designing and shipping secure, observable AI-augmented software across backend, frontend, and data surfaces. Collaborate with product, design, data, and clinical teams, use experimentation and data to validate impact, and improve engineering practices for safety, reliability, and scale.

Build and ship production-quality AI applications and reusable deployment assets (agents, orchestration, evaluation). Support enterprise rollouts, integrate with IT/cloud infrastructure, accelerate client adoption, and champion responsible AI while advising and collaborating with client teams.