Top AI Jobs in Edinburgh

Facilitate workshops to define AI/agent use cases, design and deploy Copilot Studio agents integrated with Power Platform and enterprise data, test and iterate solutions, enable scaling and governance, and provide documentation, training, and stakeholder engagement to drive adoption.

Build core AI platform capabilities (agent runtimes, model routing, tool registries) and production-grade services and tooling using Node.js, TypeScript, NestJS and React. Develop reusable AI infrastructure, work with LLMs and LangChain/LangGraph, and own features end-to-end to enable secure, scalable AI across the company.

Lead AI security technical strategy across customers and field teams. Serve as senior SME in AI security, run discovery workshops, design demos/POVs, create reusable reference architectures, and influence product, PMM, and engineering using field insights. Support strategic engagements, enablement, and thought leadership to scale AI security adoption.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Lead engineering to shift GTM from no-code automation to code-first, AI-agent-driven systems. Design scalable backend pipelines, integrate CRMs/APIs, build reusable agent frameworks, ensure reliability and observability, and hire/mentor a distributed engineering team to deliver production GTM infrastructure.

Lead design, build, and scale client-facing and internal automation systems. Create advanced workflows (GoHighLevel, n8n/Make/Zapier), integrate platforms via APIs/webhooks, document SOPs, advise clients, maintain CRMs and troubleshoot live systems for reliability and scalability.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

Design, build, and optimize AI-driven automation workflows using platforms like Zapier/Make and LLMs. Integrate APIs, develop Python/JavaScript backend logic, maintain automation pipelines for sales/marketing/support, monitor systems, troubleshoot issues, and document scalable SOPs.

Lead high-volume outbound prospecting, run discovery calls, qualify and manage high-ticket opportunities through the full sales cycle, book and conduct meetings, handle objections, collaborate with founders/sales leadership, and consistently meet outreach, meeting, and revenue KPIs.

The AI Data Expert will evaluate AI outputs, provide cultural insights, generate content, and ensure quality by applying evaluation guidelines and offering clear feedback.

The role involves evaluating AI outputs, providing annotations, and generating culturally relevant content while ensuring quality guidelines are met.

The Marketing Technology Manager builds and owns AI-powered automations and workflows, ensuring reliable systems for marketing functions and improving efficiency through technical solutions.

The AI Product Designer will develop and optimize player experiences in Avakin Life, focusing on AI-driven design, rapid prototyping, and collaboration with product and engineering teams.

Lead identification, development, and global deployment of AI and automation solutions to optimize forms and precedents content operations. Prioritize opportunities, collaborate with cross-functional teams, ensure governance and quality, integrate solutions, report outcomes to leadership, and drive continuous improvement and responsible AI practices.

Lead product execution for Ping Identity's Identity for AI solution: define strategy, prioritize features, gather market and customer requirements, produce technical requirements, manage roadmap and backlog, collaborate with engineering, design, sales and marketing, enable go-to-market, and leverage AI tools to improve product workflows and delivery.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

The Senior Director Analyst for AI Cybersecurity will provide expert analysis and guidance on leveraging AI in cybersecurity for clients, driving innovation and impact in their operations.

Lead development of ClickHouse's Python connector and SDKs, build integrations with orchestration and transformation tools, drive AI/LLM integration strategy for production data workflows, engage with open-source community, and collaborate across product and platform teams to deliver enterprise-grade data integrations.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Design, build and deploy production-ready AI/ML solutions (including NLP, computer vision and generative AI) for UK public sector clients. Implement MLOps, develop scalable data pipelines and API-driven services, integrate Azure AI/LLM platforms, ensure responsible AI practices, and support proposals, PoCs and knowledge sharing across consulting teams.