Compensation
£40,000 – £70,000 base salary + performance-related bonus + benefits
Performance-Related Bonus
Great benefits (listed below)
TL;DR
- Role: AI Engineer (Agentic AI & LLM)
- Level: Engineer or Senior
- 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.