Top AI Jobs in London

Own end-to-end implementation and product development for strategic enterprise accounts, design AI evaluation frameworks and prompts, build agentic AI workflows and internal tooling, run UAT and track production analytics, and collaborate with engineering, design, and client teams including on-site client visits.

Lead design, build, and scaling of AI-powered features across the platform. Prototype, ship, and optimise end-to-end production systems; work closely with clinicians and customers; use analytics and user research to measure impact and prioritise work tied to commercial outcomes.

Lead evaluations of data center readiness for high-density AI deployment, ensuring compliance with construction and technical standards across various global regions.

Lead design and delivery of AI-enabled supply chain planning solutions using Kinaxis Maestro. Own solution architecture for forecasting, intelligent workbooks, autonomous agents, integrations, and deployment. Partner with planning and business leaders to define use cases, data architecture, governance, and adoption while mentoring teams and ensuring end-to-end delivery and hypercare.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

As a Policy & Risk Research Intern, you'll track updates across AI regulations, translate legal provisions for product logic, and draft materials to support strategic positioning and engagement with regulators.

The Communications Intern will support narrative intelligence, ghostwrite for the founder, and manage strategic communication channels, focusing on AI policy engagement.

As a Principal Forward Deployed Engineer, you'll deploy JetBrains Central in enterprise environments, implement AI governance, mentor teams, and advise customers on AI-native development best practices.

The Senior AI Engineer will design and implement AI systems, focusing on production-grade solutions using LLMs and agentic systems to meet business needs.

The Senior Software Engineer will enhance the reliability of AI systems, implement monitoring frameworks, manage failover systems, and lead incident responses, focusing on serving infrastructures for large language models.

As a Technical Product Manager (AI), you'll manage projects, prototype tools, and enhance AI insights for decision-making at Verve.

Lead design and scaling of legal AI evaluation frameworks and benchmarks, build and maintain datasets, audit and score AI legal outputs, define metrics focused on reasoning and citation accuracy, and collaborate with engineering to translate legal errors into model fine-tuning feedback.

This role leads CX automation and AI initiatives, builds AI agents, manages complex programmes, and enhances operational tools for team effectiveness.

Own full enterprise GTM lifecycle for a GenAI SaaS product: prospecting, discovery, demos, pilots, negotiations, closing, and expansion. Map creative workflows, design pilots with success metrics, manage complex multi-stakeholder sales cycles, coordinate contracts and security reviews, and build repeatable GTM processes while feeding customer insights back to Product and Leadership.

As the primary engineer for Aperture OS, you will own the full codebase, drive feature development, manage API and AI integrations, and ensure security and scalability in a multi-tenant SaaS environment.

Build and ship AI-native systems for a fintech neobank: AI agents, RAG/LLM apps, tool-calling and automations to improve onboarding, KYC, support, risk review, CRM and operations, focusing on reliability, latency, cost, security and compliance while collaborating with product and engineering teams.

Architect and build a production, self-improving learning platform: capture event pipelines, measure outcomes, implement recommendation and feedback loops, maintain knowledge graphs and vector stores, build feature stores and experimentation infra, and create evaluation frameworks. Spend majority time coding and shipping resilient systems while providing technical leadership and shielding the engineering team.

Lead design and implementation of scalable AI-centric systems for offensive security, develop agentic AI capabilities (LLMs, RAG), own feature lifecycle to production, mentor engineers, run design reviews, and integrate AI into microservice architectures with cloud, container, and messaging technologies.

The Research Manager will lead AI-powered insight projects, manage client relationships, oversee team execution, and innovate using AI tools to improve insights delivery.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Author and iterate curriculum for experienced engineers transitioning to system architecture: validate syllabus, define measurable learning objectives, produce and fine-tune lessons with AI tools, design hands-on projects and rubrics, respond to LX feedback, and deliver milestone-based content over a 4–6 month, 15–20 hr/week engagement.

Lead enterprise sales for Everpure's AI/HPC data infrastructure, driving large deals with F1000 companies. Build executive relationships, lead cross-functional pursuit teams, influence product roadmap, manage a pipeline to exceed revenue targets, and evangelize all-flash storage and data pipeline solutions for AI workloads.

Own end-to-end design, build, deploy, and iterate on production AI systems that improve lead quality, reduce refunds, and automate workflows. Ship customer-facing AI features, agentic workflows, retrieval and context layers, reliable event-driven automations, evaluation datasets and monitoring, and reusable components while connecting technical decisions to business KPIs.

Design, build, and maintain developer tooling for the MAX AI inference stack, including profilers, tracers, debuggers, dashboards, and agent-native tools. Integrate tooling across MAX, Mojo, and driver APIs, collaborate with kernel/model/serving engineers, and drive performance analysis, debugging, and observability to improve developer productivity and production reliability.