Top AI Engineer Jobs in London

Lead development of secure, high-performance, low-latency cloud-based payments applications using Java and AI-assisted engineering practices. Responsibilities include designing and coding payment flows, reviewing and debugging code, establishing secure coding and automated testing standards, supporting production stability, coaching engineers on responsible AI adoption, evaluating architectures, and promoting modern software engineering practices across the organization.

Develop and support secure, scalable software for AI/ML data platforms. Responsibilities include Python architecture and development, cloud-native and containerized application delivery, code review, testing, troubleshooting, operational stability, and automation of recurring remediation. The role also promotes validated AI-assisted engineering practices and collaborates with product managers and data strategy teams. Preferred experience includes graph databases such as AWS Neptune and generative AI engineering pipelines.

Evaluates and quality-controls legal AI skills, workflows, and use cases; researches prompting and applied AI methods; develops best-practice exemplars; tests new product capabilities; trains Legal Engineers; supports executive demonstrations; coordinates global skills libraries; and contributes approximately 30% of time to live client work.

Build and evolve core backend systems that review AI safety detections, identify ambiguous cases, automate triage, and close model feedback loops. Partner with product, design, and ML teams to own projects end-to-end, improve detection precision, and raise quality, operability, and engineering clarity across Samsara's Safety platform.

Lead the design, development, deployment, and continuous improvement of AI/ML solutions for enterprise customers. Build integrations using Python or JavaScript, APIs, microservices, and enterprise systems; analyze data; automate workflows; address AI risk, privacy, and compliance; and align technical solutions with business goals. Mentor engineers and communicate effectively with technical and non-technical stakeholders in complex, ambiguous environments.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

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.

Build and operate AI backend systems, agentic workflows, RAG pipelines, evaluations, and LLM-powered data pipelines. Develop data-layer capabilities, monitoring, inspection interfaces, and production-grade software across Python, Dagster, PostgreSQL, Elasticsearch, and Iceberg. Collaborate with product, research, and commercial teams, own technical projects and PoCs, and balance security, quality, and delivery speed in a high-ownership startup environment.

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.

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.

Partner with startups to identify high-impact Codex use cases, build AI coding workflows and integrations, resolve technical issues, and drive sustained adoption. Lead technical talks, demos, workshops, hackathons, and developer programs. Create reusable code examples, guides, and tools, while translating customer feedback into improvements for OpenAI’s Product, Research, and Engineering teams.

Design, build, and operate AI-native production systems and agentic workflows at scale. Integrate LLMs and observability to automate incident detection and remediation, maintain CI/CD and IaC pipelines, champion engineering best practices, mentor teams, and ensure compliance within a regulated financial environment.

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 and operate production AI systems for intelligence analysts, including multi-agent orchestration, retrieval, graph analytics, streaming pipelines, APIs, databases, and cloud infrastructure. Own services end to end across Python and Go, evaluate AI improvements using live traffic, integrate ML models, and collaborate with analysts, product, data, and research teams. The role requires autonomous technical leadership, strong systems engineering, production reliability, observability, security, and rapid experimentation.

Build and operate production AI backend services in Python and Go, including multi-agent orchestration, retrieval, graph analytics, APIs, databases, and streaming pipelines. Own well-defined features end to end, evaluate improvements against analyst traffic, integrate ML models, and improve system reliability, latency, cost, and scalability. Collaborate with analysts, product, data, and ML teams while contributing to cloud infrastructure, deployment, and production operations.

Lead developer engagement for firmwide developer tools to drive safe, practical adoption of AI coding tools. Run demos, workshops, and enablement; create guidance, examples, and playbooks; gather feedback; partner with platform, risk, and engineering leaders to scale adoption across a regulated enterprise.

Build and scale shared AI platform capabilities on Azure, with emphasis on agentic AI runtimes, orchestration, tool integration, LLMOps, AgentOps, governance, observability, and reliability. Develop reusable templates and reference implementations, secure model and enterprise-tool access, support lifecycle evaluation and CI/CD practices, and establish cost-aware, production-ready patterns for AI agents. Partner with product, cloud infrastructure, security, and engineering teams to enable safe, scalable enterprise AI adoption.

Serve as a technical advisor to high-growth companies building products with Claude. Partner with engineering teams through pair programming, architecture reviews, code contributions, prototypes, evaluation frameworks, documentation, and scalable AI application design. Create developer resources, identify cross-engagement patterns, support hackathons and workshops, and collaborate with Anthropic’s product and engineering teams. The role requires production LLM application experience, strong Python skills, customer-facing communication, and travel to customer sites.

Build end-to-end AI-first product features, design scalable APIs and frontends with React/TypeScript, integrate LLMs and AI agents, collaborate with founders and customers, and establish engineering culture and infrastructure.

Build and deploy scalable AI applications, intelligent agents, and AI-powered features in production. Collaborate with research, data science, and product teams; evaluate emerging AI technologies; and promote responsible AI through bias detection, explainability, and safety alignment. The role requires production machine learning experience, strong Python and AI framework skills, cloud and MLOps experience, and expertise in deep learning, NLP, and large language models.

Partner with enterprise customers to identify AI use cases, validate feasibility, and deliver tailored applications, templates, and integrations using WRITER’s platform, APIs, Knowledge Graph, and LLMs. Translate complex AI concepts into actionable recommendations, collaborate with Product and Engineering, improve technical integration processes, and drive customer adoption, value, and expansion.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows for enterprise customers. Own system architecture from proposal through deployment, develop React and TypeScript interfaces, and partner with DevOps on continuous deployment, monitoring, logging, performance, and security. Collaborate with product, data science, and engineering teams while mentoring peers and delivering resilient production software.

Build and deploy production-grade agentic AI applications, generative AI features, RAG pipelines, and LLM integrations for customer engagements. Develop Python and FastAPI services, model data with relational and graph databases, implement governance guardrails, and instrument systems for observability. Collaborate with platform engineering on deployment and MLOps/LLMOps, modernize legacy workflows, troubleshoot reliability issues, and contribute reusable AI patterns and best practices.