Top AI Jobs in Edinburgh

As a Presales AI/ML Engineer at Neptune, you'll be presenting the product capabilities to AI research and ML teams globally, crafting compelling narratives to accelerate their work and help them achieve AI breakthroughs.

This role involves helping to train generative AI models by crafting and answering computer science questions and evaluating AI-generated code. It is a freelance, remote opportunity with flexible hours.

As a Data & AI Consultant, you will act as a strategic advisor, working with clients to identify and implement Data & AI solutions that drive business value. Responsibilities include leading client engagements, collaborating on use cases, advising on data governance, and educating executives on relevant technologies.

Join Contrast Security as an ML Developer to design, develop, and deploy ML models for cybersecurity. Collaborate with teams to integrate and improve models, develop the AI/ML platform, and mentor team members. Stay updated on ML trends and technologies.

Lead engagements with customers from presales to implementation of cloud services with a focus on Data & AI. Responsible for solution ownership, customer workshops, requirements documentation, and project estimation. Collaborate with sales teams and Data & AI Delivery Lead to ensure alignment and necessary expertise. Part of the senior leadership team guiding the development of skills within the Data and AI team.

The Course Development Engineer will create and improve technical courses related to Data and AI, ensuring quality and alignment with learning objectives while collaborating with course review engineers.

As a Solutions Principal, you'll customize AI/ML solutions, partner with sales teams, and guide solution proposals for large businesses in various sectors. You will oversee service-led sales pursuits, manage customer expectations, and evangelize technology solutions while collaborating with cross-functional teams at Dell.

The AI/ML and MLOps Field Engineer at Canonical will assist global companies in adopting AI using open source technologies on cloud infrastructures, primarily focusing on Linux and Kubernetes. Responsibilities include architecting cloud solutions, engaging in customer interactions, and solving complex data architecture problems using various data technologies.