Top AI Engineer Jobs in London

The Applied AI Engineer will develop AI features for consumers, optimize AI tools, and enhance workflows in the energy sector.

Work directly with enterprise customers to design, implement, and deploy production-ready AI agents using IFS Loops. Integrate with customer systems, tune agent behavior, troubleshoot deployments, iterate for performance and adoption, and document/handoff stable solutions to engineering or support teams.

You'll oversee the post-training pipeline, improve AI models, ensure production code quality, and collaborate with teams to enhance product user experience.

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.

Lead the design and delivery of GenAI solutions, leveraging LLMs and RAG architectures while integrating with enterprise systems. Focus on production-grade implementations and collaboration across teams for digital transformation efforts.

Build and operate production agentic AI systems and LLM-powered features. Implement RAG pipelines, extend shared AI infrastructure, monitor and debug production models, and collaborate with product and design to deliver secure, reliable AI capabilities in a regulated financial environment.

Design, build, and operate production-grade agentic AI and LLM-powered features and RAG pipelines over sensitive financial data. Collaborate with product and design, contribute shared AI infrastructure (LLM services, orchestration, evaluation), and own reliability, security, auditability, and compliance in production.

Build and ship production-grade agentic AI systems and LLM-powered features. Architect RAG pipelines over sensitive financial data, contribute shared AI infrastructure, own production reliability and safety, and collaborate with product and design to deliver customer-focused outcomes in a regulated environment.

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.

As a Deployed AI Engineer, you'll integrate software into systems, collaborate on customer requirements, and troubleshoot issues while showcasing AI capabilities.

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

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

Build and maintain backend features for Agent Observability across the GitLab monolith and AI gateway. Design and implement GraphQL/REST APIs, improve automated tests (RSpec), collaborate cross-functionally, troubleshoot production issues on Tier 2 on-call, and help shape responsible AI integration and platform architecture.

Lead the design and delivery of scalable, secure enterprise software using Go, distributed systems, Domain-Driven Design, event-driven architecture, and cloud-native technologies. Build production AI-native applications incorporating LLMs, RAG, tool calling, and agentic workflows. Partner with Product and Design, solve complex customer problems, mentor engineers, lead technical initiatives, and promote engineering excellence, observability, testing, security, and operational readiness.

Build and maintain on-device LLM software that transforms live neuromonitoring signals into actionable clinical insights. Responsibilities include AI integration, model evaluation, prompting, guardrails, inference optimization, medical device software development, cybersecurity and regulatory compliance, and fleet reliability. The role requires close collaboration with hardware, product, and clinical teams while ensuring safe, reliable performance on constrained hardware.

Enable firmwide adoption of generative AI by embedding with teams, providing user support, delivering workshops, creating guidance and communications, evaluating AI tools, and gathering feedback. The role translates technical capabilities into practical business workflows, supports senior stakeholders, tracks adoption barriers, and contributes to tool rollout and optimization. International travel is required as needed for training and team support.

Leads the design and delivery of AI-powered automation, Infrastructure as Code, and cloud engineering solutions across Azure and OCI. Builds reusable automation frameworks, CI/CD pipelines, secure provisioning processes, resilience and recovery capabilities, and FinOps practices. Provides technical leadership, mentors engineers, establishes engineering standards, and promotes AI-first and automation-first practices across Cloud Operations.

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

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.

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.

Own product areas for AI stockbroking and other financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; use metrics, research, feedback, and production issues to improve decisions; launch features, monitor performance, and drive iteration.

Own AI Investing product areas across financial services, defining user problems, product scope, success metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Use research, feedback, metrics, experiments, and production insights to make decisions, launch features, monitor performance, and drive continuous improvement while balancing speed, user experience, business impact, compliance, and operational complexity.

Own product areas within an AI neobank across financial services such as insurance, payments, savings, investing, and travel. Define customer problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; use research, feedback, and performance data to iterate; and balance speed, user experience, business impact, compliance, and operational complexity.