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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 working 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.
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!
Vacature Engineer in Glasgow employer: Hypercube Consulting
Contact Detail:
Hypercube Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Vacature 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 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 be shy about reaching out! If you see a role that excites you on our website, drop us a message. Express your interest and ask any questions you might have – it shows initiative and enthusiasm!
✨Tip Number 4
Prepare for interviews by brushing up on your communication skills. Be ready to explain complex AI concepts in simple terms, as you'll need to engage with both technical and non-technical stakeholders.
We think you need these skills to ace Vacature 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 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’re the perfect fit!
Showcase Your Projects: If you’ve worked on any relevant projects, make sure to include them in your application. Whether it’s a personal project or something from your previous job, we love seeing practical applications of your skills, especially in AI and cloud environments.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details about the role and our company culture there!
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 that you've used in your projects.
✨Collaboration is Crucial
This position involves working closely with cross-functional teams, so highlight your teamwork skills. Share examples of how you've collaborated with data engineers, data scientists, or DevOps teams in the past. This will demonstrate your ability to communicate effectively and contribute to a team environment.
✨Engage with the Community
Show your passion for AI by discussing any contributions you've made to the AI community, whether through blogs, speaking engagements, or open-source projects. This not only highlights your expertise but also your commitment to staying current in the field and sharing knowledge with others.