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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Hypercube Consulting - **Location:** UK - **Experience:** Expert - **Salary:** £40,000.0 - £70,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Data Architecture, Data Cleansing, Information Engineering, Data Infrastructure, Database Queries, DevOps, Python (Programming Language), Machine Learning, Open Source Technology, Azure Machine Learning, SQL Databases, Data Streaming, AI Infrastructure, Azure Service Bus, Data Processing, Large Language Models, Prompt Engineering, Apache Spark, HybridCloud, Data Lakes, Kubernetes, HuggingFace, Apache Kafka, Free and Open-Source Software, Machine Learning Operations, Virtual Agents, Terraform, GPT, Automation Anywhere, Docker, Databricks - **Published:** May 16, 2026 - **Apply:** https://find.jobs/jobs-near-me/ai-engineer-glasgow/2770293437-2/ ## About the Role * Cloud Experience: Must have AWS or Azure (certifications desirable) * Management: No direct line management required * Consultancy/Energy Experience: Highly beneficial, non-essential * Visa Sponsorship: Not currently available; right to work and UK residency required * Flexibility: Part-time, condensed hours, job-shares, and flexible arrangements considered * Diversity & Inclusion: Extremely important-encouraging a broad mix of people from all backgrounds, * Agentic AI & LLMs: Hands-on experience building and deploying large language models and agent-based AI workflows. * Cloud AI (AWS/Azure): Experience delivering AI or ML solutions in production cloud environments. * Python: Strong capability in developing production-quality AI/ML code. * LLMOps & AI Model Management: Familiarity with tools like MLFlow, LangChain, Hugging Face, Kubeflow, or similar platforms. * Data Processing: Working knowledge of Databricks/Spark or comparable large-scale data processing tools. * SQL: Solid capabilities in data querying and preparation. * Data Architectures: Understanding of modern data infrastructure (lakehouses, data lakes, vector databases). Additional (Nice-to-Have) Skills * Infrastructure as Code: Terraform or similar. * Containers & Kubernetes: Docker, EKS/AKS. * Streaming: Kafka, Kinesis, Event Hubs. * AWS or Azure certifications. Desirable Experience * Consulting or Energy sector experience. * Public profile (blogs, conferences, open source). * Stakeholder engagement and requirements translation. * Integration with external or hybrid cloud systems. * Clear communication across diverse technical audiences. ## Description We are seeking an AI Engineer with hands-on experience in Agentic AI systems and Large Language Models to help design, develop, and deploy advanced AI solutions for our clients. You will collaborate closely with data engineering, analytics, and cloud teams to deliver transformative AI capabilities. As a senior hire in a growing organisation, your impact will be meaningful from day one. You will: * Engage clients to understand their challenges and help design Agentic AI and LLM-driven solutions. * Build and implement robust AI systems, including ML/LLM pipelines and agentic workflows. * Contribute to best practices in LLMOps, AI lifecycle management, and cloud-native AI infrastructure. * Share knowledge and support team development as we grow our AI engineering capability., Technical Delivery: * Act as a hands-on AI and LLM practitioner across client engagements and internal projects. * Deliver AI solutions leveraging modern Agentic AI architectures and LLM frameworks, working alongside our Principal AI Engineers on technical direction. End-to-End AI Delivery: * Design, build, and maintain scalable AI and LLM-based pipelines using AWS or Azure services (e.g., SageMaker, Azure ML, Databricks, OpenAI integrations). * Contribute across AI model lifecycles from data preprocessing and prompt engineering through to deployment and continuous monitoring in production environments. Collaboration & Stakeholder Management: * Work with cross-functional teams (data engineers, data scientists, DevOps, stakeholders) to deliver client-focused AI solutions. * Communicate AI and LLM concepts clearly to both technical peers and non-technical stakeholders. Knowledge Sharing: * Apply and help refine best practices in LLMOps and Agentic AI (prompt engineering, evaluation, agent architectures, CI/CD). * Engage with the AI community through blogs, speaking engagements, or open-source contributions - encouraged and supported. Business Development & Growth: * Support business development through demos, proof-of-concept work, and technical pre-sales activities. * Build strong client relationships and contribute to growing team capability over time., * High Impact: Work on energy-sector AI solutions that directly influence client outcomes. * Career Growth: Senior mentorship, dedicated training budgets, and a clear pathway to Principal. * Flexible Environment: Open to various flexible working arrangements to suit your lifestyle. * Start-up Culture: Contribute to shaping our culture, processes, and technologies. * Personal Branding: Encouraged and supported in building your public professional profile. Benefits * Performance-Related Bonus * Enhanced Pension * Enhanced Maternity/Paternity * Private Health Insurance * Health Cash Plan * Peer Cash Award Scheme * Cycle-to-Work Scheme * Flexible Remote/Hybrid Working * Events & Community Participation * EV Leasing Scheme * Training & Events Budget * Mentorship Programmes ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Got AI ideas but no money? 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