Top AI Jobs in London

Senior individual contributor researching how threat actors weaponize AI across the attack lifecycle. Instrument telemetry, analyze global data and OSINT, develop detections, automate research workflows with LLMs and scripting, and publish external threat intelligence to inform protections and operations.

Lead design and delivery of LLM platform infrastructure across AWS and on-prem GPU environments. Own roadmap, optimize training/inference, establish model/data lifecycle, drive knowledge graph strategy, coordinate cross-functional teams, select vendors, and define governance and platform engineering standards.

Lead design, build and deploy enterprise-scale AI products: agentic workflows, RAG, LLM integrations, vector DBs, APIs and microservices. Define architecture, reliability, observability and governance, mentor engineers, partner with product and data teams, and drive AI platform adoption and measurable business outcomes.

Hands-on lead full-stack engineer who designs scalable architecture, ships production SaaS features, integrates AI tools, mentors senior engineers, improves deployment pipelines, and drives engineering quality.

Deliver production-ready ML model releases: train and iterate PyTorch models, evaluate and debug performance, apply optimizations (quantisation, distillation), collaborate with performance and deployment teams, and ensure models meet tight on-vehicle runtime constraints.

Lead the Central AI Team and own the firm's AI transformation portfolio: define strategy, governance, prioritisation, readiness, value measurement and scaling. Align business, legal, technology and data stakeholders, drive board-level decisions, manage dependencies and risks, and build organisational capability for enterprise AI adoption.

Lead development of network and AI strategy, governance and roadmaps. Identify, prioritise and scale AI use cases for network planning, operations and assurance. Ensure safe, auditable deployments and partner across security, data, procurement and operating companies to deliver measurable network and business outcomes.

Lead design and delivery of production-grade ML and GenAI solutions on the Databricks platform. Architect end-to-end ML pipelines, optimize training/inference, implement MLOps/LLMOps, build MVPs for sales, influence product roadmap, mentor peers, and present technical content externally.

Lead go-to-market strategy and commercial execution for a new AI-native Market Research service. Build positioning, pricing, and metrics; generate pipeline and close deals; partner with Sales, Marketing, Product, and Research; represent Maze publicly; and help scale the service and team while feeding market insights back into product and offering design.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Deploy and maintain AI enablement infrastructure (MCP servers, AI agents, hosting) and cloud environments on AWS/GCP. Build IaC and CI/CD pipelines, monitor performance/cost, support containerized services, manage API keys and permissions, document runbooks, and collaborate with Security, Engineering, and stakeholders to improve cloud governance and AI tooling adoption.

Build and evolve a real-time AI tutoring system: design prompt architectures and agentic workflows, run experiments and custom evaluations, integrate models with platform infrastructure, prototype production model interactions, ensure safety and observability, and collaborate with educators and product teams to translate pedagogy into technical solutions.

Own and grow the Lesson Insights product end-to-end: prioritize actionable AI-driven feedback, connect per-lesson insights to long-term progress, involve tutors, scale across languages, increase engagement and retention, and run data-driven experiments and analyses.

Review, evaluate, and annotate AI-generated content (text, audio, images, video). Identify errors and quality issues, select best outputs, provide written feedback, follow detailed guidelines, attend trainings, and complete tasks remotely within deadlines.

The Lead Engineer will develop secure systems, ensure engineering best practices, lead technical architecture, and integrate AI capabilities into production systems.

Lead product for developer-focused IDP and agentic AI platform capabilities: own discovery, roadmap, and outcomes; champion developer experience (APIs/SDKs/docs/onboarding); translate customer needs into clear requirements; partner with engineering and architects; use AI to accelerate product discovery and delivery; mentor other PMs and set team standards.

Lead and grow a high-performing engineering team building scalable, customer-facing products. Own engineering delivery, architecture, code quality, observability, incident response, and engineering culture. Partner with product, design, mobile, backend and AI teams to turn ambiguous problems into production systems while coaching engineers and improving processes to deliver reliable, high-quality outcomes.

Lead and manage a cross-functional engineering team to design, build, and operate a high-scale payments app. Drive architecture, delivery, code quality, reliability, observability, and incident response while coaching engineers and improving processes to ship customer-focused products quickly and reliably.

Lead technical design and execution of products for an AI-enabled payments app. Hands-on coding, architecture, system design, code review, debugging, production ownership, mentoring engineers, improving dev tooling, and collaborating with product, design and AI teams to ship reliable, scalable systems.

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.

Lead and manage a cross-functional engineering team to design, build, and operate scalable, reliable payment and AI-integrated products. Own technical architecture, delivery, code quality, observability, incident response, and engineering processes while coaching engineers and driving product-focused outcomes.

Technical lead responsible for designing, building and operating scalable full-stack systems. Hands-on coding, architecture decisions, code reviews, mentoring, cross-team collaboration, production reliability, observability and driving features from concept to production.

Lead and manage a cross-functional engineering team to design, build, and operate scalable, reliable production systems. Own technical architecture, delivery, code quality, observability, incident response, and engineering processes while coaching engineers and driving product outcomes in partnership with product, design, mobile, backend and AI teams.

Lead end-to-end AI talent placement in London: source and screen AI-skilled candidates, build employer relationships, manage placement lifecycle and retention, hit placement targets (5-8/month), run diversity outreach (40%+ women), maintain candidate records and reports, and provide market intelligence to improve UtivaHire and UtivaLearn.

Lead customer success initiatives by managing client relationships, driving user adoption and satisfaction, and influencing product development based on customer feedback.