Top AI Jobs in London

Hunt and close new business across UK private sector for cloud, platform engineering, Data & AI, managed services, and AI enablement. Own full sales cycle from prospecting to close, engage senior technical stakeholders, develop proposals, position Wayfinder, and work with delivery, engineering, marketing and partner teams to shape opportunities and pipeline.

Lead the AI/ML/Data team at Tripledot Studios, driving initiatives to enhance games and monetization while managing a high-performing team.

Lead the roadmap and delivery of Ohme's data and AI products: prioritize features, define requirements and success metrics, work hands-on with ML/data engineers to ship and iterate, liaise with commercial and ops teams, and drive adoption and measurable business outcomes.

The Senior/Staff Software Engineer, AI will develop AI-powered features, design backend services, and optimize workflows, ensuring integration and user experience. The role requires collaboration across teams to innovate and improve AI applications.

Seeking artists with AI-assisted visual development skills. Portfolio showcasing creative workflows is crucial. Strong foundations in visual art and curiosity about production workflows are desired.

Lead Hansen's shift to AI-first engineering by defining AI-enabled workflows, building prototypes, implementing tools like Copilot and Claude Code, coaching teams, establishing governance and metrics, and scaling adoption to improve developer productivity, delivery speed, and software quality.

As a Staff Cloud Site Reliability Engineer, you will build and scale AI cloud platform reliability, define operational standards, and lead SRE initiatives.

The role involves building and scaling the reliability of AI cloud platforms, overseeing GPU compute infrastructure, incident response, and operational excellence while collaborating with various teams to enhance system efficiency and resilience.

The AI Business Development Specialist will identify business opportunities, build client relationships, and promote AI solutions in various sectors, with a focus on the MENA region.

Lead design and delivery of secure, scalable AI solutions across enterprise programmes. Define AI architectures, governance, Responsible AI, and reusable patterns. Provide architectural oversight, support presales and discovery, build prototypes and accelerators, and guide delivery teams from discovery to production and ongoing model management.

Embed with client teams to design, deploy, and scale production AI platforms end-to-end. Deliver measurable business outcomes via rapid prototyping, productionization, governance, and stakeholder alignment while building reusable playbooks and patterns.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, LLM provider integration, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment, and reusable patterns while embedding with client engineering teams to measure and improve agent accuracy, latency, safety, and cost.

Design, build, and deploy production-grade multi-agent AI systems for enterprise clients. Own RAG pipelines, multi-LLM integration, LLMOps (eval, prompt governance, observability), orchestration frameworks, and lead client engineering engagements and engineering teams.

The Senior Manager - Innovation and AI will lead the innovation technology strategy, focusing on AI and digital transformation within a legal context, providing advisory, overseeing development, and fostering collaboration across teams.

Support productionising AI/ML solutions for Workday products under senior guidance. Collaborate with data scientists and engineers to deploy, monitor, and maintain models using cloud, containerisation, and MLOps practices while following responsible AI and data privacy guidelines.

Lead end-to-end ERP implementations and customer relationships, deliver onboarding and go-live execution, shape product roadmap with product and engineering from customer feedback, establish implementation methodologies and standards, and drive best-practice ERP deployment for finance teams.

Serve as the technical bridge between customers and internal teams to design, validate, and onboard AI infrastructure solutions across bare metal, BMaaS, Kubernetes and cloud-native platforms. Lead technical discovery, POCs, benchmarks, architecture reviews, reusable reference architectures, and customer workshops while influencing product roadmap and production readiness.

Lead technical vision and execution for agentic AI across Funds Transfer Pricing and Financial Hedging. Architect and build scalable multi-agent systems, guide LLM, RAG, and vector-data strategies, enforce AI governance and XAI, mentor senior engineers, and align technical roadmap with business objectives.

Design and build production-scale agentic AI systems for funds transfer pricing and financial hedging. Architect multi-agent workflows, RAG and embedding pipelines, integrate LLMs and predictive models, optimize for low-latency/high-throughput, and provide technical leadership and mentorship across teams.

Lead and grow a team of 5–8 AI engineers to build an enterprise AI platform delivering conversational, generative, and agentic AI features. Drive end-to-end execution, establish AI engineering best practices, maintain CI/CD pipelines, collaborate with product and platform teams, and communicate technical progress and risks to stakeholders.

Design and implement secure, production-ready AI platforms and solutions; move proofs-of-concept to large-scale production; define governance, observability, security and operational standards; support technical discovery, mentor engineers, and develop reusable patterns and accelerators across cloud and data ecosystems.

Build and own core AI developer tooling: extend the companys AI coding agent, enable spec/testing/reliability features, ensure secure data access, and help teams adopt AI-enhanced SDLC practices to measure and improve developer productivity.

Design, prototype, and deliver applied ML and agentic systems using generative AI and multi-source scientific data to improve clinical trial design. Integrate historical trials and real‑world data into production systems, ensure evaluation and regulatory-appropriate guardrails, deploy cloud-based services, and communicate results to technical and non-technical stakeholders while contributing to engineering and data science best practices.

Design, prototype, and ship applied ML/AI systems that integrate multi-source clinical and scientific data to improve clinical trial design. Build generative-AI and agentic systems, production-grade Python services (containers, APIs, CI/CD), and evaluation/guardrails for regulated environments while communicating findings to technical and non-technical stakeholders.

Lead architecture and build an automation and AI-native toolchain (fusing traditional automation with agentic workflows), mentor teams, drive cross-department adoption, standardize practices, and evolve IT operations toward infrastructure-as-code and platform-first models.