Top AI Jobs in Edinburgh

The Senior Applied AI Solution Engineer will design, develop, and lead AI applications, mentor engineers, and communicate with clients to align AI strategies with business objectives.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

The role involves building GenAI solutions, using AI tools daily to enhance software engineering processes, collaborating with teams, and establishing AI architecture standards.

Own and deliver the platform's intelligence layer end-to-end: design, build, and improve agent systems for enterprise transformation. Act as the technical authority, implement production-grade LLM-based services, ground agents in real context, collaborate with Product Owner, and drive systems engineering and shipping decisions.

Design and operate large-scale GPU infrastructure for distributed AI training, ensuring reliability, performance, and efficient customer partnerships.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

Work as a consultant combining human insight, strategy and AI to deliver client insight programmes. Synthesize diverse evidence, apply methodology and emerging technologies, support proposals and business growth, and develop consultancy and analytics skills across sectors.

Lead and deliver AI-enabled insight programs, manage client accounts, combine human, cultural and artificial intelligence methods, grow client relationships and revenue, coach and develop teams, shape propositions and ways of working to evolve insight consultancy.

Architect, design and deliver production-grade AI solutions using ML, generative and agentic AI. Lead technical delivery, implement RAG/prompt engineering, ensure responsible AI, drive cloud-native, scalable architectures, mentor and manage junior engineers, and collaborate with clients to translate business needs.

Lead design, build, and operate the AI Engineering Platform to deploy and manage AI agents. Provide technical leadership, architecture, code review, mentorship, DevOps ownership, observability, model evaluation, and platform governance while collaborating with product and delivery teams.

Lead the AI Engineering platform team to define technical vision, build reusable pipelines, tooling, plugins and frameworks, contribute code, ensure engineering excellence, manage and grow senior engineers, align technical strategy across the organisation, own OKRs, manage vendor partnerships, and enable developer productivity and reliable production operations.

Lead design, build, release and operate the AI Engineering Platform. Develop scalable production software, define architecture, review code, ensure operational stability, drive AI/LLM integration and evaluation, improve developer productivity, and document system decisions while collaborating across product and engineering teams.

Support full-cycle recruiting for product, engineering, and business roles: source and screen candidates, coordinate interviews, maintain ATS data, assist offers, and run recruiting projects and process improvements.

Lead design, development, and delivery of production AI systems and orchestration for insurance workflow automation. Architect inference pipelines, routing, caching, observability, and fallback strategies. Drive latency, reliability, cost, and scalability decisions, debug distributed production incidents, define engineering standards, and mentor teams to ship reliable AI features.

Build and operate backend systems and orchestration layers for LLM-powered insurance workflows. Implement inference pipelines, optimize latency/throughput/cost, ensure observability, debug distributed production issues, and collaborate across product, ML, and operations to deliver reliable, scalable AI services.

Design, build, and optimize LLM-powered product features integrated into insurance workflows. Develop prompts, reasoning flows, guardrails, and validation layers; evaluate model performance with experiments and user feedback; collaborate with backend, product, and design to ship reliable, production-ready AI automation and decision-support features.

Join the AI & Innovation team to build and maintain full‑stack Python services, integrate ML/LLM models into production, develop scalable APIs and data pipelines, optimise AWS infrastructure, and write unit/integration tests to ensure reliability for high‑traffic jobseeking products.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

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.

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

The role requires creating insights for finance leaders focused on AI integration in finance processes, conducting research, and advising on AI strategies. It involves engaging with clients, presenting insights, and mentoring junior team members.