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- Tasks: Design and deploy 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 support 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.
- 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).
Integration with external or hybrid cloud systems.
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!
Technisch engineer. in Glasgow employer: Hypercube Consulting
Contact Detail:
Hypercube Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Technisch engineer. 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 meet potential employers and learn about job openings that might not be advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Agentic AI and LLMs. This will give you an edge when chatting with recruiters or during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and cloud experience. Practice coding challenges and be ready to discuss your past projects in detail, especially how you've tackled real-world problems.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Technisch engineer. in Glasgow
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to 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. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about transforming the energy sector with AI. Share specific examples of your past projects and how they relate to the role. Let us know why you’d be a great fit for our team!
Showcase Your Technical Skills: We love seeing technical skills in action! If you’ve worked with tools like MLFlow, LangChain, or Databricks, make sure to include that in your application. Demonstrating your familiarity with these platforms will definitely catch our eye.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it’s super easy to do!
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 your hands-on experience with these technologies, as well as any relevant projects you've worked on. This will show that you're not just familiar with the concepts but can also apply them in real-world scenarios.
✨Cloud Experience is Key
Since the role requires cloud experience, particularly with AWS or Azure, ensure you can talk about your experience with these platforms. If you have certifications, mention them! Be prepared to discuss specific services like SageMaker or Azure ML and how you've used them in your previous roles.
✨Collaboration is Crucial
This position involves working closely with cross-functional teams, so be ready to share examples of how you've successfully collaborated with data engineers, data scientists, and other stakeholders. Highlight your communication skills and how you can explain complex AI concepts to non-technical audiences.
✨Show Your Passion for AI
Engage with the AI community by mentioning any blogs, speaking engagements, or open-source contributions you've made. This shows your enthusiasm for the field and your commitment to staying updated with the latest trends and best practices in AI and LLMOps.