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- Tasks: Design and develop advanced AI solutions for the energy sector using cutting-edge technology.
- Company: Join a rapidly growing data and AI startup transforming the energy industry.
- Benefits: Competitive salary, performance-related bonus, flexible working, and health benefits.
- Other info: Flexible work arrangements and opportunities for personal branding and community engagement.
- Why this job: Make a real impact in AI-driven energy solutions while growing your career.
- Qualifications: Experience with Agentic AI systems, LLMs, and cloud platforms like AWS or Azure.
The predicted salary is between 40000 - 70000 £ per year.
Compensation £40,000 – £70,000 base salary + performance-related bonus + benefits.
Role: AI Engineer (Agentic AI & LLM)
Cloud Experience: Must have AWS or Azure (certifications desirable).
Flexibility: Part-time, condensed hours, job-shares, and flexible arrangements considered.
Diversity & Inclusion: Hypercube Consulting is a rapidly growing data and AI startup dedicated to transforming the energy sector through cutting-edge technology. Specialising in advanced AI systems, including Agentic AI workflows and large language models (LLMs), we help clients unlock profound value from their data assets. Join our expert team in shaping the future of AI-driven energy solutions.
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.
- 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.
- 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.
- 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.
- 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.
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).
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.
Personal Branding: Encouraged and supported in building your public professional profile.
Performance-Related Bonus, Enhanced Pension, Private Health Insurance, Health Cash Plan, Cycle-to-Work Scheme, Flexible Remote/Hybrid Working, Events & Community Participation, Training & Events Budget.
Diversity & Inclusion: We're excited to explore how your expertise can help transform data and AI in the energy sector!
Engineer gezocht in Glasgow employer: Hypercube Consulting
Contact Detail:
Hypercube Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Engineer gezocht in Glasgow
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and energy sectors. Attend meetups, webinars, or industry events to make those valuable connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Agentic AI and LLMs. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Don’t just apply blindly! Tailor your approach for each role. Research Hypercube Consulting and understand their mission in transforming the energy sector. Mention how your experience aligns with their goals when you reach out.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in joining our team at Hypercube Consulting.
We think you need these skills to ace Engineer gezocht in Glasgow
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the AI Engineer role. Highlight your hands-on experience with Agentic AI systems and LLMs, and don’t forget to mention your cloud experience with AWS or Azure!
Showcase Your Skills: We want to see your technical prowess! Include specific examples of projects where you've built and deployed AI solutions. Mention any tools like MLFlow or Hugging Face that you’ve used to manage AI models.
Communicate Clearly: Remember, we value clear communication! When writing your application, explain complex AI concepts in a way that’s easy to understand. This will show us you can engage with both technical and non-technical stakeholders.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in transforming the energy sector.
How to prepare for a job interview at Hypercube Consulting
✨Know Your AI Stuff
Make sure you brush up on your knowledge of Agentic AI systems and large language models. Be ready to discuss specific projects you've worked on, especially those involving AWS or Azure. This will show that you’re not just familiar with the concepts but have practical experience too.
✨Showcase Your Cloud Skills
Since cloud experience is a must, be prepared to talk about your hands-on experience with AWS or Azure. Highlight any relevant certifications you have and be ready to explain how you've used these platforms to deliver AI solutions in production environments.
✨Communicate Clearly
You’ll need to explain complex AI concepts to both technical and non-technical stakeholders. Practice simplifying your explanations and think of examples that illustrate your points. This will demonstrate your ability to bridge the gap between tech and business.
✨Engage with the Community
Mention any contributions you've made to the AI community, whether through blogs, speaking engagements, or open-source projects. This shows your passion for the field and your commitment to staying updated with the latest trends and best practices.