Top AI Engineer Jobs in London

Build and operate data pipelines across Salesforce, HubSpot, Stripe, and Databricks; maintain go-to-market analytics models and dashboards; develop LLM-based agents and automations for Sales, Marketing, and Finance; and productionize integrations with testing and monitoring.

Embeds with hardware engineering teams to build agentic workflows and AI skills that perform engineering tasks, including requirements checks, test-plan analysis, and issue investigation. Demonstrates value to customers, identifies reusable patterns, and translates customer needs into product capabilities. Requires strong Python and data skills, customer-facing communication, modern AI coding-agent experience, and willingness to travel. Hardware, test, controls, forward-deployed, solutions engineering, and security-clearance experience are advantageous.

Support ML engineering teams running production training and inference workloads across Kubernetes, cloud, Linux, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through tooling, automation, documentation, runbooks, and operational processes. The role is hybrid in London, requires at least two office days weekly, and supports EMEA shifts.

Drive AI-led transformation across Legal, People, and FP&A by identifying workflow pain points, mapping end-to-end processes, and building reliable AI agents, automations, integrations, and internal tools. Advise on build-versus-buy decisions and technology stacks while implementing security, privacy, compliance, monitoring, human oversight, evaluation, and audit controls. Partner with frontline teams, executives, and engineers to deliver and continuously improve internal AI products.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop training and inference pipelines, improve model quality and efficiency, create evaluation datasets and metrics, monitor production performance, debug issues, and ship reliable ML-powered features. The role emphasizes balancing accuracy, relevance, latency, cost, privacy, and reliability while collaborating with engineering and product teams.

Lead technical design and delivery of AI-native email applications. Own architecture and implementation across backend, frontend, APIs, databases and cloud. Stay hands-on writing and reviewing code, debugging production, mentoring engineers, driving major initiatives, improving tooling, and ensuring reliability, security and observability while collaborating with product, design and AI teams.

Build and prototype full-stack web, CMS, marketing platform, API, and cloud infrastructure solutions. Support technical exploration across CMS modernization, AI, agentic systems, and workflow automation. Develop proof of concepts, investigate scalable platform capabilities, and collaborate with principal engineers, technical leaders, engineers, and stakeholders to shape pragmatic architecture and roadmap decisions. The role uses modern JavaScript technologies, cloud services, APIs, and infrastructure-as-code.

Build and operate a production Azure data and AI platform. Responsibilities include Azure landing zones, network architecture, identity, governance, monitoring, Databricks and Unity Catalog administration, data integrations, MLOps pipelines, model deployment, CI/CD, infrastructure as code, security scanning, self-service tooling, documentation, and on-call support. The role requires hands-on Terraform, Azure, Databricks, containerization, observability, and Python or PowerShell experience.

Build and operate an Azure-based data and AI platform through hands-on infrastructure engineering. Responsibilities include designing Azure landing zones and networks, managing governance and identity, deploying Databricks and data services, building MLOps pipelines, implementing DevSecOps practices, creating self-service tooling, and supporting platform incidents. The role requires Terraform, Azure, Databricks, CI/CD, containerization, security, observability, and Python or PowerShell expertise.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Lead development of AI agent products and AI-native applications for hardware engineering workflows. Prototype ideas rapidly, build products end to end, improve agent user experiences, establish interaction patterns, and lead technical direction across product engineering. Partner with design, product, and platform teams while working closely with engineers and users.

Build and rapidly prototype AI-native product features for hardware engineers. Develop full-stack interfaces, APIs, streaming agent workflows, citations, editable outputs, and trustworthy human-agent handoffs. Own product decisions from concept through customer delivery, observe real users, and iterate on usability. The role requires strong front-end development with TypeScript and React, experience with AI-powered interfaces, rapid prototyping, product ownership, and modern AI coding-agent workflows.

Own production Python data pipelines, ETL/ELT workflows, and the ontology powering an AI platform. Integrate warehouses and business applications with graph databases and vector stores, while managing deployment tooling, cloud infrastructure, monitoring, and security. The role begins as hands-on individual contribution and develops toward technical leadership through mentoring and architecture ownership.

Leads end-to-end development and architecture of enterprise Generative AI and Agentic AI products. Partners with business and technology stakeholders, shapes product roadmaps, builds AI agents using Google ADK and MCP, writes production-quality Python or Java, and drives testing, CI/CD, security, compliance, mentoring, and technical strategy.

Build and operate high-throughput backend services for financial crime detection across WorldRemit and Sendwave. Responsibilities include developing alerting, screening, risk-rating, and agentic AI investigation workflows; integrating third-party compliance platforms; improving observability, reliability, and auditability; collaborating with compliance teams; reviewing designs and code; and participating in on-call production support. The role requires strong Java and Spring expertise, event-driven systems experience, and familiarity with cloud and infrastructure tooling.

Design, build, evaluate, deploy, and operate production AI systems using LLMs, retrieval, agents, predictive ML, and model adaptation. Translate ambiguous business problems into reliable system behavior, establish evaluation and monitoring practices, troubleshoot complex failures, and improve AI-enabled product, engineering, and operational workflows. Collaborate across architecture, platform, security, data, quality, product, and domain teams while mentoring engineers and owning systems through continuous improvement.

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.

Build production machine-learning features that transform raw AI usage data into customer-facing insights. Own classification, clustering, data pipelines, model deployment, evaluation frameworks, ground-truth datasets, error analysis, uncertainty quantification, and production reliability. Collaborate with customers, product, engineering, and security teams to shape insight experiences. Work primarily in Python across a polyglot Java, Spring Boot, and TypeScript stack deployed on AWS.

Build and deploy production AI agents and full-stack automations for insurance claims. Identify workflow bottlenecks by working directly with claims handlers, turn opportunities into shipped features, and own system reliability, correctness, and risk. Collaborate with AI engineers and operations while scaling real-world claims automation in an office-based London environment.

Design, architect, and implement production-grade agentic AI systems. Drive agentic product direction, identify opportunities, build prototypes into scalable production code, integrate with partners and platforms, and establish evaluation, observability, and regression-testing practices. Collaborate closely with Product and support customer and partner technical integrations while raising engineering standards across the applied-AI team.

Design, build, deploy, and optimize production AI systems, including LLM integrations, RAG pipelines, embeddings, vector search, predictive models, and inference systems. Collaborate with product and engineering teams to translate business needs into scalable solutions for recommendations, risk scoring, forecasting, and workflow automation. Own data pipelines, monitoring, evaluation, security, privacy, documentation, and continuous model improvement across cloud environments.

Deliver and deploy ML and generative AI solutions across industries: design experiments, build production-scale models, identify business use cases, drive measurable value, consult with clients, and mentor team members.

Design, build, and operate scalable data pipelines, models, cloud platforms, and AI applications on Google Cloud. Lead technical delivery for client projects, make architecture decisions, manage risks, and ensure production quality through testing, observability, and data controls. Build and evaluate LLM applications, collaborate with consultants and data scientists, communicate with technical and non-technical stakeholders, and raise engineering standards through reviews, documentation, and reusable practices.

Build and maintain infrastructure, data systems, and tools for evaluating and deploying AI/ML models supporting scientific drug discovery. Partner with scientists, product managers, ML experts, and DevOps engineers to define requirements, implement algorithms, optimize GPU performance, develop cloud-native systems, and own features through production. Contribute to architecture, testing, code reviews, documentation, and engineering practices while mentoring and collaborating across teams.

Build and maintain scalable AI/ML scientific applications, backend systems, data infrastructure, and model evaluation tools for drug discovery. Partner with scientists and product leaders to define requirements, implement algorithms, optimize performance, and shape architecture and engineering practices. The role requires expertise in machine learning, statistics, GPUs, Kubernetes, databases, data lakes, and cloud-native systems, with preferred experience in biological or imaging data, research environments, workflow automation, GenAI, or agents.