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For Companies At a Glance
- Tasks: Design and develop advanced AI solutions for the energy sector using cutting-edge technology.
- Company: Join Hypercube Consulting, a fast-growing startup transforming the energy industry with AI.
- Benefits: Enjoy a competitive salary, performance bonuses, flexible working, and great health benefits.
- Other info: Be part of a diverse team committed to innovation and personal growth.
- Why this job: Make a real impact in AI-driven energy solutions while growing your career.
- Qualifications: Experience with Agentic AI, LLMs, and cloud platforms like AWS or Azure is essential.
The predicted salary is between 40000 - 70000 £ per year.
Compensation: £40,000 – £70,000 base salary + performance-related bonus + benefits
Performance-Related Bonus
Great benefits (listed below)
Location: UK-based, fast-growing technology startup specialising in the energy sector
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
Who We Are
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.
Role Purpose
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.
Key Responsibilities
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.
Technical Skills & Experience
Please apply even if you meet only some criteria—we value potential alongside experience.
Core Skills
- 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.
What's in It for You?
- 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
Diversity & Inclusion
Hypercube is committed to creating an inclusive environment reflective of society. We actively encourage applications from all backgrounds and experiences.
Ready to Apply?
If this role excites you, please apply via our careers page or reach out directly—even if you meet some but not all criteria. We're excited to explore how your expertise can help transform data and AI in the energy sector!
N.B. Visa sponsorship is currently not available.
AI Engineer employer: Hypercube Consulting
Contact Detail:
Hypercube Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer
✨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
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with AWS or Azure, and how you've tackled challenges in previous roles. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates who are excited about transforming the energy sector with AI.
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Engineer role. Highlight your experience with Agentic AI and LLMs, and don’t forget to mention any cloud experience you have 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 AI in the energy sector and how your background makes you a great fit for our team. Let us know what excites you about this opportunity at Hypercube Consulting.
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 a previous job, we love seeing practical examples of your work with AI systems and cloud technologies.
Apply Through Our Website: We encourage you to apply directly through our careers page. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at Hypercube!
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 and large language models. Be ready to discuss your hands-on experience with these technologies, as well as any projects you've worked on that showcase your skills in building and deploying AI solutions.
✨Cloud Experience is Key
Since the role requires cloud experience, particularly with AWS or Azure, be prepared to talk about your familiarity with these platforms. Highlight any relevant certifications you have and share specific examples of how you've used cloud services to deliver AI solutions.
✨Collaboration is Crucial
This position involves working closely with cross-functional teams, so be ready to discuss your experience collaborating with data engineers, data scientists, and other stakeholders. Share examples of how you've effectively communicated complex AI concepts to both technical and 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 on the latest trends and best practices in AI and LLMOps.