Top AI Engineer Jobs in London

Forward Deployed AI Engineers develop Gen AI solutions by building workflows, implementing strategies, and solving real-world problems at scale with clients.

Design, build, test, and deploy scalable AI services integrated with operational systems. Develop Python services, REST APIs, ETL/data pipelines, IaC, and monitoring; collaborate with AI specialists and operations stakeholders to improve workflows, controls, and production readiness in Markets Operations.

Lead end-to-end delivery of AI Agents on the Moveworks ServiceNow platform: design, build, integrate, tune, and launch custom LLM-based automation solutions while partnering with customers, product, and engineering to drive adoption and product feedback.

Lead and mentor an Applied AI team to design, train, evaluate, and deploy large-scale NLP/LLM, graph, and agentic ML solutions; collaborate with business, tech, and control partners; develop firm-wide ML frameworks; research and prototype advanced methods.

Design, build, and deploy scalable ML services combining NLP, CV, and statistical methods. Collaborate with engineers, cloud and SRE teams to productionize models, ensure observability and compliance, and maintain models over time. Communicate results to stakeholders and document processes for regulatory needs.

Design, build, and productionize autonomous and assistive multi-agent AI systems. Implement RAG and semantic search with vector databases, integrate tools/APIs, apply prompt engineering and guardrails, orchestrate microservices workflows, instrument agents for metrics, run evaluations/A-B tests, and mentor teammates.

Lead technical design and development of AI-native systems for credit pre-trade markets. Build scalable AI platforms, modernize legacy systems, deliver secure production code, partner with trading and product teams, support production operations, evaluate vendors, and drive engineering best practices and communities of practice.

Lead development and enhancement of equity derivatives trading and analytics applications, drive automation and AI-assisted engineering practices, collaborate with trading and quant teams, maintain global platform stability, and lead the Systematic Quoting Platform workstream for EMEA.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Lead a technical area driving design, development, and delivery of AI/ML and data platform solutions. Own architecture, governance, and adoption of AI-assisted engineering practices, ensure operational stability, and influence stakeholders while hiring and developing engineering talent.

Lead design and delivery of production-grade AI/ML and agentic solutions for the LLM Suite platform. Own architecture, implementation, and operational stability using public cloud services and agentic frameworks. Build GenAI services, integrate LLMs, ensure secure high-quality code, and improve platform scalability and reliability through collaboration and communities of practice.

Design, build, and operate LLM inference backend services focused on routing, batching, scheduling, streaming, and quota management. Implement APIs/SDKs, profile and optimize performance (CPU, memory, network, GPU), improve reliability and observability, support CI/CD-safe deployments, and learn LLM serving fundamentals while applying responsible AI and enterprise-authorized AI-assisted development tools.

Lead research and engineering of state-of-the-art ML solutions (NLP, LLMs, recommendation systems). Drive experimentation, design training frameworks, build scalable production deployments, collaborate with cross-functional teams, publish research, and develop firm-wide ML frameworks while adhering to compliance requirements.

Lead design and build developer-facing platform tooling (IDE experiences, web tools, test infra, SDLC workflows). Drive AI-assisted engineering adoption, implement static analysis, testing, CI/CD, cloud test execution, and governance to improve developer productivity and code quality.

Lead design and delivery of scalable GenAI platforms and services: build RAG pipelines and agents, model/version management, monitoring and drift mitigation, cloud-native microservices and deployment, testing and guardrails to reduce hallucinations, ensure compliance, and mentor engineering teams.

Build and deliver production-ready, LLM-enabled full-stack applications: backend services, data/LLM pipelines, vector search, and front-end UIs. Embed with customers onsite to deploy, iterate, codify reusable AI assets, shape platform tooling, and ensure scalable, observable, secure enterprise deployments.

Design, build, and deploy production-grade generative AI and multi-agent systems for financial services. Architect scalable MLOps pipelines, optimize LLMs and multi-modal models, implement RAG and prompt-engineered agent workflows, and collaborate with clients and cross-functional teams to accelerate GenAI adoption and deliver measurable business impact.

As a Lead AI Engineer, you'll architect and deploy generative AI systems, optimize large models, and lead teams in AI engineering across financial services.

Lead AI architecture and engineering for enterprise agentic AI systems. Define standards for RAG, agents, orchestration, evaluation, security, and governance; provide hands-on technical leadership, mentor engineers, and align cross-functional stakeholders to deliver secure, compliant AI solutions.

Build full-stack AI-enabled applications and backend services integrating MCP servers, LLMs, RAG retrieval, and agent orchestration. Design reusable MCP tools/skills, implement secure RBAC, ensure observability, and prepare deployments for OpenShift/Kubernetes while collaborating across product and platform teams.

The Senior Data Scientist will unify technical vision, develop and deploy ML models, and enhance AI strategy across teams. Responsibilities include collaboration, ownership of projects, and maximizing value through AI and data science techniques.

Design, build, and operate reliable, scalable AI/ML platform infrastructure. Develop observability, resilience, security, and automation tooling, own NFRs, participate in on-call rotations, troubleshoot production issues, and collaborate cross-functionally to support large-scale AI deployments.

Design and build evaluation, observability, and reliability infrastructure for AI/LLM/agent workflows. Define SLOs/SLIs, monitoring, drift detection, testing pipelines, and guardrails. Partner across platform, product, security, and data science teams to enable safe, scalable AI adoption and participate in on-call rotations.

Conduct hardware-aware AI research: generate ideas, design and run ML experiments, implement performant scalable systems, publish and present results, and collaborate with researchers, silicon and software engineers to guide next-generation AI hardware.

Build and maintain backend AI capabilities for GitLab Duo Chat: design flow components and agentic flows in Python/LangGraph, integrate LLMs and multi-agent orchestration, implement and maintain APIs in the Rails monolith, improve observability and reliability, and participate in on-call rotations and cross-functional collaboration to deliver safe, scalable AI chat features.