Top AI Jobs in London

Design and govern scalable, secure AI, data and automation solutions across pre-sales and delivery. Lead architecture, produce technical artefacts and roadmaps, advise customers on AI strategy, ensure governance and responsible AI, mentor teams, and support solution shaping through implementation to operational adoption.

Lead Moonshot's applied AI safety portfolio, overseeing red teaming, adversarial evaluation, client relationships, team leadership, method development, and business growth across harms like extremism, CSEA, and crisis. Translate subject-matter expertise into testable evaluation frameworks, advise AI companies and regulators, ensure ethical and legal compliance, and drive portfolio strategy, funding, and external engagement.

Full-cycle recruiter responsible for sourcing and closing elite Research Scientists, Software Engineers, and Robotics engineers. Partner closely with founders and technical leaders, build scalable sourcing and screening workflows (Ashby/ATS), run a fast, candidate-friendly hiring process, and use data and automation to optimize pipeline health and conversions.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines, kernels, and networking. Implement low-level CUDA/cuDNN/Triton kernels, tune hardware-software interactions, and develop monitoring and debugging tools for large-scale training runs to maximize utilization and reduce time-to-convergence.

Manage curriculum development for data science and engineering, collaborate with instructors, create content, and drive improvements based on learner feedback.

As a Curriculum Manager, you will manage content development for the data science and engineering curriculum, collaborating with instructors and teams to enhance learning experiences.

Own and improve Primer's GTM intelligence: maintain dashboards and semantic models, train BI/LLM models (Omni/Claude), surface pipeline/account signals, ensure correct Salesforce modeling, build revenue-driving models and GTM AI workflows, partner with data engineering and RevOps, drive adoption, and promote self-serve data literacy.

Lead technology and AI strategy for the AI Business Solutions practice, focusing on Dynamics 365, Power Platform and Microsoft AI. Define architecture, governance, repeatable Copilot/agent patterns, and technical standards; engage C-suite customers and Microsoft; drive capability investment, IP/accelerators, and senior technical capability development to scale commercially viable AI solutions.

Lead applied research and development of deep learning, NLP, and multimodal LLM solutions for personalized learning. Prototype, productionize, and optimize AI tutors; collaborate with product and engineering; mentor junior scientists; and establish ML best practices to improve learner outcomes.

Design, build, evaluate, and operate production-grade LLM and agentic systems. Integrate models, vector stores, and enterprise data sources; implement retrieval, tool calling, orchestration, evaluation pipelines, observability, and secure deployment. Prototype rapidly and harden solutions into maintainable production systems with documentation and runbooks, collaborating across product, engineering, and customers.

Lead design, development, and production deployment of generative AI solutions on Azure and Databricks. Build LLM-driven applications (RAG, agentic workflows) using LangGraph/LangChain, implement backend APIs with FastAPI, containerize with Docker, deploy on Kubernetes/AKS, architect search/retrieval with Azure AI Search and MongoDB, and mentor engineers while driving architecture, monitoring, and responsible AI practices.

Provide legal advice on AI, emerging technologies and enterprise digital transformation. Draft, review and negotiate technology agreements (SaaS, cloud, licensing, outsourcing), support AI governance and privacy/regulatory compliance across jurisdictions, partner with IT, Procurement, Privacy and InfoSec, and help manage legal risk throughout technology project lifecycles.

Lead Lovable's visual identity using generative AI as the main production method. Build AI-executable visual systems (style references, fine-tunes/LoRAs, prompt libraries), direct high-volume AI output across campaigns and touchpoints, define brand guidelines for an AI-native workflow, collaborate closely with a Brand Engineer to create scalable tooling, and produce both machine-generated and hand-crafted brand assets as needed.

Design and deliver AI, ML and advanced analytics solutions on the Microsoft Azure stack from discovery through production. Lead technical direction, run consultative workshops, manage client stakeholders, support presales, mentor colleagues, and contribute to internal and external communities.

Conduct original AI safety research by designing and running experiments on transformer-based LLMs, applying white-box and black-box interpretability methods, developing reproducible Python research code and tooling, documenting results in papers and reports, and translating findings for external partners and stakeholders.

Embed in client teams to scope ambiguous AI and policy/communications briefs, build bespoke AI-powered tools and prototypes using the studio tech stack, lead client engagements end-to-end, develop internal products, and translate technical solutions for non-technical stakeholders while contributing to the wider communications and policy practice.

Coordinate client approval and implementation processes for AI pilots, managing questionnaires, security assessments and cross-functional inputs. Serve as primary contact, track milestones, prepare high-quality responses from knowledge libraries, escalate risks, maintain documentation repository, and streamline processes to enable accurate, timely pilot launches and excellent client experience.

Lead design, implementation, and production operation of agentic LLM systems for medical imaging clinical trials. Build end-to-end agentic workflows (planning, retrieval, tool use), RAG solutions, evaluation frameworks, scalable backend services, and MLOps pipelines. Provide technical leadership, mentor engineers, ensure observability, compliance, and safety, and translate clinical workflows into validated, production-grade AI systems.

Design, build, and operationalize agentic AI systems for clinical imaging trials: intake/validation (DICOM/non‑DICOM), RAG-grounded workflows, QC checks, evaluation harnesses, MLOps deployment, observability, safety/traceability, and cross-functional enablement across the PDLC.

Design and build core backend systems for an agent platform: orchestration, durable execution, memory, tool integration, observability, and evaluation. Create scalable services/APIs, develop retrieval and long-term memory solutions, monitor and improve agent behavior, and help drive technical direction and team growth for AI-driven collaboration features.

Senior Director analyst providing thought leadership and actionable research on infrastructure and operations AI strategy. Produce insights, presentations, and client advisories; guide AI infrastructure, cloud adoption, integration, governance, and vendor selection. Lead research agenda, mentor analysts, collaborate cross-functionally (infrastructure, engineering, enterprise architecture, cybersecurity), support sales, and represent Gartner at events. Drive process improvements and influence senior stakeholders to align infrastructure and operations with business outcomes.

Design polished, user-centric mobile and web interfaces for BJAK's AI and financial products. Create wireframes, flows, prototypes and high-fidelity UI, evolve the design system, collaborate with PMs and engineers, iterate using user feedback and analytics, and ensure accessibility, responsiveness and visual consistency.

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 identification, design, build, and adoption of AI-driven automations and internal tooling across GTM, product, finance, ops, and engineering. Partner with functional leads, prototype code-first workflows and low-code automations, work closely with engineers to improve developer productivity, and maintain internal knowledge systems for compounding value.

Design and build scalable AI-powered backend services and APIs in Golang for cloud-native, microservices-based platforms. Develop developer tools, ensure observability, CI/CD, and performance, solve distributed-systems challenges, and mentor engineering teams to deliver production-ready solutions.