Top AI Jobs in Edinburgh

The VP Field Marketing is responsible for leading marketing strategy and execution in Europe, focusing on demand generation, brand awareness, and pipeline development while collaborating cross-functionally and driving a data-driven culture.

Design and implement large-scale AI/ML systems using LLMs and RAG. Develop data pipelines, optimize models, and collaborate with teams on actionable intelligence solutions.

Build, deploy, and operate secure, scalable production software and platform components that support machine learning and generative AI. Collaborate with data scientists and product partners to translate prototypes into reliable services, own features end-to-end, maintain code quality, observability, and participate in incident response and CI/CD workflows.

The Senior AI Engineer will design, build, and deploy generative AI systems, collaborating with clients to drive AI adoption and deliver business impact in financial services.

As an AI Transformation Owner at GitLab, you will shape AI strategies, improve workflows, and drive adoption of AI solutions in your organization, working closely with an AI Engineer.

Lead research and deliver executive-level insights on enterprise AI strategy, producing thought leadership, advising clients and vendors, supporting sales and conferences, mentoring analysts, and driving research agenda and process improvements to accelerate clients' AI value realization.

As a Staff Backend Engineer at GitLab, you'll enhance backend infrastructure for CI, integrate AI features, and ensure reliability and performance in CI/CD workflows.

Lead the design and development of generative AI systems, focusing on LLMs and MLOps pipelines within financial services, guiding teams in strategic AI adoption.

Lead R&D stakeholder discovery to define a digital strategy, prioritise initiatives using desirability/viability/feasibility, build a value-linked roadmap, and produce decision-ready recommendations for pilots and transformation.

Assess current and target R&D technology landscape, define architecture principles for data and AI, identify integration and PLM dependencies and risks, score technical feasibility, and translate findings into executive recommendations to support roadmap delivery.

Support engagements through stakeholder interviews, analysis, synthesis, and document production. Maintain trackers, perform maturity and dependency assessments, draft slide content and executive-ready materials, and assist the Engagement Lead to ensure high-quality, timely strategy deliverables.

Manage the Onboarding Risk team across regions, ensuring high-quality risk decisions, developing team members, and owning onboarding risk procedures. Support complex escalations, collaborate cross-functionally, and keep processes compliant with card scheme rules and payments legislation while improving KPIs and decision frameworks.

Lead and scale multi-team AI engineering and data science organization to design, deliver, and govern GenAI, LLM, Vision AI, and multi-agent knowledge products; own end-to-end solution design, Agile delivery, quality standards, production validation, stakeholder alignment, hiring, and organizational capability building.

Ownership of backend features for Agentic Tools: design and implement GraphQL/REST APIs, build secure scalable Ruby on Rails services, improve RSpec automated tests, collaborate across product and AI teams, participate in Tier 2 on-call, and shape architecture for AI agent interactions with GitLab.

Build, ship, and maintain backend features enabling AI agents to interact with GitLab. Design GraphQL/REST APIs, extend tests (RSpec), work with PostgreSQL, troubleshoot production issues, and collaborate cross-functionally to integrate AI tooling responsibly.

Lead and scale an AI Security incubation team to drive technical GTM strategy, recruit and mentor principal AI security specialists, enable global sales with repeatable POVs and playbooks, advise Fortune 500 C-levels on safe AI deployments, and feed field insights into Product and Engineering to drive revenue growth.

Lead field-facing AI security strategy for enterprise customers by designing secure LLM/RAG architectures, delivering demos and POVs, advising on data protection and unsanctioned AI usage, and scaling enablement and product feedback across sales, product, and engineering teams.

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.

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.

As a Senior Product Manager, you will define and evolve GitLab's AI Platform Management capabilities, focusing on AI Control Plane and strategic governance. You'll lead product discovery, drive stakeholder alignment, and collaborate across teams to deliver effective management solutions for AI usage.

As an AI Subject Matter Expert, you will guide the application of AI in core DevOps, partnering with stakeholders to enhance developer productivity and software delivery.

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

Lead identification and scoping of AI opportunities, run discovery workshops, translate business problems into functional requirements, prioritise AI initiative pipeline, work with data scientists and stakeholders to design AI solutions, and promote business analysis best practices across the department.

Design user-centred digital experiences from concept to polished UI, create purposeful motion and micro-interactions, prepare production-ready assets across web, email, presentations and AV, optimise assets for performance and accessibility, use AI tools to speed production, and collaborate within cross-functional teams to raise design quality.

Design, implement, deploy, and operate production-grade AI systems powered by LLMs. Build LLM applications, orchestration, RAG retrieval systems, integration layers, scalable infrastructure, observability, and CI/CD. Collaborate with product and engineering teams and iterate from prototype to production.