Top AI Engineer Jobs in London

Leads a team building secure, production-grade AI-driven software systems. Responsibilities include software architecture, Java or Python development, AI/ML integration, LLM and generative AI applications, algorithm validation, automated testing, CI/CD, cloud-native development, troubleshooting, and operational reliability. The role also establishes responsible AI practices, validates AI-assisted engineering outputs, mentors engineers, translates business requirements into technical roadmaps, and ensures timely delivery through Agile methodologies.

Develop and deliver production AI/ML solutions across the full lifecycle using Agile methodologies. Translate business requirements into technical specifications, build secure production code and algorithms, create architecture artifacts, analyze large datasets, develop visualizations, troubleshoot systems, and identify improvements. The role requires expertise in machine learning, Python, Spark, distributed computing, AI frameworks, agentic workflows, and AWS services, while contributing to engineering communities and emerging technology initiatives.

Develop and productionize generative AI products and augmented reality Lenses focused on image and video generation, editing, and LLMs. Build diffusion and flow-matching pipelines, evaluate open-source models and third-party APIs, and rapidly ship consumer-facing solutions. Collaborate with Product, Engineering, Lens Content, and Data Science teams to prototype, integrate, monitor, and improve models using A/B testing and user feedback. Lead complex technical initiatives while staying current with machine learning research.

Design, build, test, and maintain scalable full-stack web applications and integrations using Java, Python, and React. Implement AI-enabled features, event-driven data pipelines, and marketing integrations (CDP/CRM/CMS). Improve code quality via AI-assisted tools, CI/CD, observability, and security/compliance best practices while collaborating with product, marketing, and partners.

Build secure, scalable AI/ML platforms and applications to productionize research. Collaborate with AI researchers and data scientists, implement CI/CD and IaC, support experimentation environments, automate remediation, mentor junior engineers, and promote secure, responsible AI development practices.

Build and scale a distributed platform supporting engineering simulations, agentic applications, and physical AI workflows. Responsibilities include designing distributed architecture, developing production Python services, optimizing distributed compute, implementing workflow orchestration, and contributing to CI/CD, testing, deployment, observability, reliability, and incident response. The role also involves mentoring engineers, making technical decisions, collaborating cross-functionally, interviewing candidates, and supporting end-to-end platform operations.

Lead the architectural evolution of an AI platform from monolithic to distributed systems. Design agent-ready services, write production Python, build distributed compute and workflow orchestration, and own CI/CD, testing, deployment, observability, reliability, and scaling. Mentor engineers, establish technical standards, guide cross-team decisions, collaborate with stakeholders, and support hiring. The role requires deep distributed systems, Kubernetes, AWS, workflow orchestration, data processing, security, and production reliability expertise.

Design, build, and maintain secure, tested, performant backend integrations connecting GitLab agents with tools such as Slack, Claude Code, and Codex. Own complex features, develop GraphQL and REST APIs, improve automated testing, troubleshoot production issues through on-call rotations, enhance observability and resiliency, and collaborate across product, frontend, backend, and AI engineering teams.

As an Applied AI Engineer, you will build AI applications for public sector clients, create and evaluate datasets, and collaborate with teams to address challenges using AI solutions.

Design, build, and maintain AI agents and automated workflows for marketing. Identify automation opportunities, deploy and monitor agents, integrate with marketing tools, and report efficiency and business impact. Ensure data quality, privacy, and documentation, and enable marketing teams to adopt automations.

Build and ship agentic LLM systems for Treasury: cash monitoring, funding execution, narrative generation, and discovery-to-deployment pipelines. Create reusable agent infrastructure, governance, evaluation, and partner with engineering to ensure data accuracy while embedding with operators to scope and iterate solutions.

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.

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.

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 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 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.

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 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.

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.

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.