Top AI Engineer Jobs in London

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

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 AI Research Engineer will develop AI models to enhance product autonomy, analyze extensive data, and deliver impactful solutions, driving innovative product features.

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.

As a Software Engineer in the Core AI team, you will design and operate AI model serving infrastructure, build centralised servers, and improve developer experiences through secure, scalable platforms.

The Staff AI Engineer will build, deploy, and maintain AI applications, manage data pipelines, and lead technical designs while mentoring engineers.

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 AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

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 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.

Serve as technical authority for AI infrastructure solutions, guiding customers from use-case ideation to implementation. Architect scalable, secure AI-optimized networking, compute, and observability environments; run proofs-of-value; align Ciscos AI stack with partners; influence product roadmap and represent Cisco externally. Work closely with account executives and C-suite stakeholders to drive technical sales and complex deal cycles.

Design, build, evaluate, and operate production-grade LLM and agentic systems. Integrate models, vector stores, and enterprise data sources; implement retrieval, tool calling, orchestration, evaluation pipelines, observability, and secure deployment. Prototype rapidly and harden solutions into maintainable production systems with documentation and runbooks, collaborating across product, engineering, and customers.

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 Applied AI Engineer will design, build, and deploy LLM-powered AI features, improving workflows while engaging directly with customers to debug and enhance the system.

Lead design, implementation, and production operation of agentic LLM systems for medical imaging clinical trials. Build end-to-end agentic workflows (planning, retrieval, tool use), RAG solutions, evaluation frameworks, scalable backend services, and MLOps pipelines. Provide technical leadership, mentor engineers, ensure observability, compliance, and safety, and translate clinical workflows into validated, production-grade AI systems.

Design, build, and operationalize agentic AI systems for clinical imaging trials: intake/validation (DICOM/non‑DICOM), RAG-grounded workflows, QC checks, evaluation harnesses, MLOps deployment, observability, safety/traceability, and cross-functional enablement across the PDLC.

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 design and delivery of LLM platform infrastructure across AWS and on-prem GPU environments. Own roadmap, optimize training/inference, establish model/data lifecycle, drive knowledge graph strategy, coordinate cross-functional teams, select vendors, and define governance and platform engineering standards.

Lead design, build and deploy enterprise-scale AI products: agentic workflows, RAG, LLM integrations, vector DBs, APIs and microservices. Define architecture, reliability, observability and governance, mentor engineers, partner with product and data teams, and drive AI platform adoption and measurable business outcomes.

Hands-on lead full-stack engineer who designs scalable architecture, ships production SaaS features, integrates AI tools, mentors senior engineers, improves deployment pipelines, and drives engineering quality.

Deliver production-ready ML model releases: train and iterate PyTorch models, evaluate and debug performance, apply optimizations (quantisation, distillation), collaborate with performance and deployment teams, and ensure models meet tight on-vehicle runtime constraints.

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 evolve a real-time AI tutoring system: design prompt architectures and agentic workflows, run experiments and custom evaluations, integrate models with platform infrastructure, prototype production model interactions, ensure safety and observability, and collaborate with educators and product teams to translate pedagogy into technical solutions.

The Lead Engineer will develop secure systems, ensure engineering best practices, lead technical architecture, and integrate AI capabilities into production systems.