Top AI Jobs in Edinburgh

As a Technical Lead, you will guide software engineering teams, translate client requirements into applications, ensure best practices, and shape technical direction while mentoring team members.

The Solutions Consultant will implement data observability solutions, manage B2B software projects, and collaborate with customers for successful data architecture and deployment.

As a Technical Product Manager (AI), you'll manage projects, prototype tools, and enhance AI insights for decision-making at Verve.

Lead design and scaling of legal AI evaluation frameworks and benchmarks, build and maintain datasets, audit and score AI legal outputs, define metrics focused on reasoning and citation accuracy, and collaborate with engineering to translate legal errors into model fine-tuning feedback.

This role leads CX automation and AI initiatives, builds AI agents, manages complex programmes, and enhances operational tools for team effectiveness.

Own full enterprise GTM lifecycle for a GenAI SaaS product: prospecting, discovery, demos, pilots, negotiations, closing, and expansion. Map creative workflows, design pilots with success metrics, manage complex multi-stakeholder sales cycles, coordinate contracts and security reviews, and build repeatable GTM processes while feeding customer insights back to Product and Leadership.

As the primary engineer for Aperture OS, you will own the full codebase, drive feature development, manage API and AI integrations, and ensure security and scalability in a multi-tenant SaaS environment.

Build and ship AI-native systems for a fintech neobank: AI agents, RAG/LLM apps, tool-calling and automations to improve onboarding, KYC, support, risk review, CRM and operations, focusing on reliability, latency, cost, security and compliance while collaborating with product and engineering teams.

Architect and build a production, self-improving learning platform: capture event pipelines, measure outcomes, implement recommendation and feedback loops, maintain knowledge graphs and vector stores, build feature stores and experimentation infra, and create evaluation frameworks. Spend majority time coding and shipping resilient systems while providing technical leadership and shielding the engineering team.

Lead design and implementation of scalable AI-centric systems for offensive security, develop agentic AI capabilities (LLMs, RAG), own feature lifecycle to production, mentor engineers, run design reviews, and integrate AI into microservice architectures with cloud, container, and messaging technologies.

The Research Manager will lead AI-powered insight projects, manage client relationships, oversee team execution, and innovate using AI tools to improve insights delivery.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Author and iterate curriculum for experienced engineers transitioning to system architecture: validate syllabus, define measurable learning objectives, produce and fine-tune lessons with AI tools, design hands-on projects and rubrics, respond to LX feedback, and deliver milestone-based content over a 4–6 month, 15–20 hr/week engagement.

Lead enterprise sales for Everpure's AI/HPC data infrastructure, driving large deals with F1000 companies. Build executive relationships, lead cross-functional pursuit teams, influence product roadmap, manage a pipeline to exceed revenue targets, and evangelize all-flash storage and data pipeline solutions for AI workloads.

Own end-to-end design, build, deploy, and iterate on production AI systems that improve lead quality, reduce refunds, and automate workflows. Ship customer-facing AI features, agentic workflows, retrieval and context layers, reliable event-driven automations, evaluation datasets and monitoring, and reusable components while connecting technical decisions to business KPIs.

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.

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.

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