Top AI Engineer Jobs in London

As a Staff Backend Engineer at GitLab, you'll enhance backend infrastructure for CI, integrate AI features, and ensure reliability and performance in CI/CD workflows.

Design, build and deploy multi-agent, agentic AI systems integrating LLMs and vision-language models into production pipelines. Deliver end-to-end features for cloud, on-prem and air-gapped environments, implement observability and evaluation frameworks, and act as a technical authority for agentic AI within cross-functional teams.

Lead the design, build, and productionisation of Fundment's AI systems and infrastructure on GCP. Define AI strategy, architect LLM/RAG/agent solutions, implement MLOps, model governance, IaC, monitoring, cost optimisation, and mentor a multidisciplinary team to embed AI into products and operations.

Validate and automate testing for AWS serverless, event-driven and modernized applications. Design and run functional, integration, contract, performance, resilience and security tests. Build automated test frameworks, integrate with CI/CD, leverage AWS Kiro for AI-assisted test generation and validation, and support migration/regression testing across microservices.

Build and maintain product features that streamline property transactions. Use LLMs and AI coding agents to accelerate development, debug issues, and iterate rapidly. Own tasks from day one, collaborate with a small team, and apply practical problem-solving to deliver customer-facing improvements.

Support sales by designing and demonstrating Cisco cloud, data center, and AI-ready infrastructure solutions. Engage customers to assess modernization goals, craft platform architectures, present technical value, run workshops, and collaborate with account teams and partners to drive adoption of compute, storage, networking, and cloud-native technologies.

Design, build, and deploy production-scale AI systems including multi-agent workflows, RAG pipelines, and fine-tuned LLMs. Deliver full-stack web and backend integrations, develop evaluation frameworks and monitoring, iterate prototypes into production, and shape internal best practices for applied AI.

The role involves embedding with business teams to identify AI opportunities, manage product development, and train teams in AI applications, with a focus on measurable business outcomes.

Design, build, and maintain Python-based AI applications and intelligent workflows. Integrate AI/ML models and LLM APIs, implement APIs and microservices, optimize data pipelines, ensure scalability, security, CI/CD, and collaborate across teams to deliver production-grade AI features.

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.

Design, build, and deploy Python-based microservice APIs that call LLMs and generative models to automate business processes and support game services. Translate business requirements into API specs, integrate services across systems, collaborate with DevOps for cloud deployments, optimize AI model performance, write and debug core AI code, and communicate with cross-functional stakeholders.

Work with enterprise customers to identify problems, design and build LLM-powered agentic systems, ship solutions to production, drive adoption, and contribute reusable context-learning and evaluation components to the platform.

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 AI Engineer designs and implements AI solutions, transforms business requirements into AI assistants, and ensures integration with existing systems while supporting data processing and monitoring.

Lead the design, build, and deployment of AI-powered agents, workflows, and integrations for global marketing. Evaluate buy-vs-build, integrate platforms (HubSpot, Salesforce, Meta, Google, LinkedIn), optimize AI economics, and measure ROI while collaborating with marketing teams to automate and improve campaign execution and content localization.

Design, build, test and deploy machine learning and generative AI models for production; prepare and refine datasets; integrate AI services with applications and cloud; document models, pipelines and APIs; monitor, evaluate and improve model performance; collaborate with engineers, data specialists and business teams.

Embed with customers to model and solve large-scale scheduling and resource-planning problems. Configure, extend, and debug the .NET/Angular-based scheduling engine, ship working solutions under tight deadlines, and translate field learnings into core product improvements. Combine applied optimization, production AI techniques, and customer engagement to meet performance targets.

Build and own full-stack AI platform systems: backend services, data pipelines, frontends, and cloud infrastructure. Integrate LLM agents with lab data, instruments, and databases; prototype and harden agentic workflows; write well-tested code; mentor teammates and drive engineering best practices in a research-focused environment.

Contract AI Engineer to build an enterprise AI capability: design agentic workflows and Copilot extensions across Microsoft 365 and Salesforce, build production-ready agent solutions (connectors, monitoring, cost controls), translate sales methodology into real-time coaching and CRM insights, and collaborate with GTM leadership to drive behaviour change. Hybrid role in London for 4-6 months (3-4 days/week).

You will design, build and refine AI systems for legal applications, focusing on LLM agents and data evaluation frameworks.

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

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

Build and scale shared AI platform foundations on Azure, focusing on agentic AI. Design templates, reference implementations, secure model access, LLMOps patterns, observability, CI/CD, and operational reliability while partnering with product, cloud, and security teams.

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

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.