Top AI Engineer Jobs in London

Design, develop, and maintain scalable full-stack applications using Python and React. Build RESTful APIs, backend services, and third-party integrations; deploy and manage applications on AWS; use AI-assisted coding tools such as Claude Code and Cursor; and ensure performance, security, scalability, and maintainability. Collaborate on architecture, code reviews, CI/CD, testing, troubleshooting, and production optimization.

Partner with nonprofits, research institutes, academic medical centers, and public-sector funders to apply Claude to clinical development for rare and neglected diseases. Design and assess clinical trials, draft protocols and statistical analysis plans, identify operational risks, and support drug repurposing using biomedical data. Build production LLM tools, MCP servers, agent skills, benchmarks, and evaluation systems while collaborating with clinicians, scientists, engineers, and external partners.

Build Java backend tooling that integrates AI into coding, testing, deployments, defect triage, observability, and CI/CD workflows. Evaluate and safely integrate LLM developer tools with Jira, Confluence, GitLab, and internal systems. Automate manual engineering processes, improve testing and release practices, track delivery metrics, support responsible AI adoption, collaborate with security and compliance, and mentor engineers.

Lead the design, development, deployment, and operation of production-grade agentic AI systems supporting insurance underwriting and portfolio management. Build evaluation, observability, testing, tracing, fallback, and reliability capabilities. Partner with underwriters and portfolio managers to translate commercial needs into product and technical decisions. The role requires strong software architecture, cloud deployment, AI framework, governance, and operational expertise, with ownership from initial design through production delivery.

Build and operate applied and agentic AI workflows, SaaS platforms, integrations, and automation pipelines. Administer configurations, access, environments, and lifecycle management while monitoring service health, cost, usage, and performance. Partner with engineering, security, vendors, and business teams to implement secure integrations and automations. Conduct testing, evaluation, incident triage, documentation, governance, and continuous reliability improvements.

Own and improve production real-time voice systems for an AI receptionist. Responsibilities include building LiveKit voice agents, optimizing latency, turn detection, interruptions, speech recognition and synthesis, managing SIP and telephony integrations, handling provider failures, and measuring call quality through tracing and repeatable evaluations. The role requires strong Python or TypeScript/Node.js skills, WebRTC and SIP knowledge, and experience safely shipping changes to live services.

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.

Develop and deploy Claude-powered automation for scientific laboratories, connecting AI to lab robots, instruments, scheduling systems, and LIMS. Build production software, evaluations, reusable cookbooks, and documentation while identifying physical-world safety limitations. Shape product, engineering, and research direction through field experience, represent Claude to scientific audiences across EMEA, and collaborate directly with external labs and partners. The role involves significant travel and requires strong communication, scientific judgment, and hands-on engineering skills.

Design and deploy production-scale AI systems, develop and optimize machine learning and neural network models, work with large language models, refine prompt engineering techniques, and use OpenAI and Anthropic APIs. Collaborate with interdisciplinary teams to build AI applications for the finance sector while keeping current with industry developments.

Build and scale an AI-powered financial crime investigation platform across React and Python stacks. Design LangGraph agent workflows, RAG pipelines, integrations, and production infrastructure while improving reliability, observability, and performance. Own complex features from design through deployment, mentor engineers, shape architecture and engineering practices, and collaborate with Product, Design, Compliance, and engineering teams.

Leads a new developer productivity team focused on AI coding agents and secure engineering workflows. Builds agent runtimes, developer interfaces, verification systems, CI/CD integrations, and deployment safeguards. Owns the path from agent task through healthy robot deployment, including access controls, monitoring, rollback, simulation, and hardware-in-the-loop testing. Recruits and leads engineers while remaining hands-on, partnering across developer tools, security, safety, simulation, and robotics to improve engineering velocity, quality, cost, and safety.

Develop advanced automation and visual data processing workflows using AI tools, focusing on computer vision and data streaming techniques while ensuring system reliability and scalability.

Own the React and TypeScript web client for an enterprise AI agent product. Build streaming, trustworthy interfaces for generated content and actions, with explicit user consent and transparent agent behavior. Lead front-end architecture, component systems, accessibility, performance, security, testing, observability, and production delivery. Collaborate across service boundaries and help shape emerging protocols including MCP Apps, AG-UI, and A2UI, while advancing Lit and Web Components adoption.

Build and deliver production-grade AI and GenAI solutions for enterprise clients, including agentic systems, RAG pipelines, automation workflows, and model orchestration layers. Lead AI transformation engagements from strategy and architecture through deployment and scaling. Support presales, thought leadership, proposition development, mentoring, and technical capability building while advising clients on AI architecture, evaluation, cost, latency, scalability, and responsible AI.

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 Tailscale’s internal AI-enablement tools, workflows, guardrails, and practices. Partner with engineering leadership and technical teams to define the AI roadmap, evaluate coding agents and models, improve developer velocity and quality, and drive safe adoption. The role includes internal tooling, developer advocacy, documentation, impact measurement, and collaboration across engineering and non-engineering teams.

Build, deploy, and maintain AI and machine learning solutions across the full development lifecycle. Develop models, prepare data, contribute to MLOps pipelines, implement production monitoring, apply responsible AI principles, collaborate with clients and internal teams, and mentor junior technical staff. The role requires Python and C# proficiency, cloud AI platform experience, machine learning library expertise, knowledge of model evaluation, and experience handling complex datasets.

Lead the modernization of trading operations through AI, machine learning, and automation. Manage trade lifecycles, P&L attribution, reconciliations, data integrity, risk monitoring, and performance reporting across multi-asset trading activities. Build agentic AI workflows, predictive risk models, anomaly detection tools, automated commentary, and real-time dashboards using Python, SQL, LLMs, and MLOps technologies.

Lead architecture and hands-on implementation of scalable decision automation and production ML systems for Candy Crush Saga. Build reusable decisioning foundations, improve experimentation and causal inference tooling, mentor senior engineers, and ensure reliability, observability, and cross-team alignment for player-facing personalization and adaptation at scale.

Leads the design, development, deployment, and governance of enterprise AI solutions. Identifies high-value AI opportunities, builds scalable production applications, evaluates emerging technologies, establishes engineering standards, and supports responsible AI adoption. Partners with architects, engineers, product teams, and senior stakeholders while translating technical concepts for non-technical audiences. Also mentors engineers and helps establish Railpen’s AI capability, governance, and best practices.

Build and operate reliable, highly available HPC infrastructure for AI model training and inference. Responsibilities include observability, automation, deployment, scaling, failover, incident management, postmortems, security, compliance, and collaboration with ML and platform engineering teams. The role requires operating CPU/GPU clusters, distributed systems, storage, networking, workload schedulers, and ML pipelines while optimizing capacity and costs.

Lead the research, development, testing, deployment, and maintenance of complex machine learning and generative AI models. Design MLOps pipelines for CI/CD, monitoring, retraining, and production operations. Solve complex technical problems, evaluate emerging AI technologies, deliver high-risk prototypes, integrate models with existing systems, and ensure model safety, security, performance, and reliability. Mentor junior team members and provide specialist generative AI expertise for public sector and national security projects.

Deliver end-to-end Palantir Foundry solutions, including data pipelines, transformations, ontology models, integrations, and production deployments. Translate client requirements into scalable, governed, high-performance data applications using Python, SQL, and PySpark. Collaborate with business and technical stakeholders, lead solution design and implementation, support adoption, and provide technical guidance. The role also contributes to data architecture, testing, CI/CD, data quality, security, and platform engineering best practices.

Build and deploy production-grade AI solutions across data ingestion, feature engineering, model training, evaluation, and deployment. Develop LLM integrations, agentic workflows, RAG architectures, dashboards, backend APIs, and prompt-engineering solutions. Collaborate with fixed-income business users, engineering, data engineering, and application support teams. Maintain production applications, write tested and documented code, and evaluate emerging AI/ML technologies.

The Senior AI Agentic Engineer will oversee product strategy and architecture for AI projects, lead cross-functional teams, and manage client interactions across financial services.