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Open nowFirst seen 58 minutes ago

AI Engineer

Hypercube Consulting5 open roles

Where
Glasgow, United Kingdom
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Your applicationOpen nowAI EngineerHypercube Consulting · Glasgow, United Kingdom

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market. Hypercube Consulting postings stay open a median of 6 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: first seen 58 minutes ago

Hypercube Consulting median: 6 days open

The posting

Compensation

£40,000 – £60,000 base salary + performance-related bonus + benefits

TL;DR

  • Role: AI Engineer (Agentic AI & LLM)
  • Location: UK-based, fast-growing technology startup specialising in the energy sector
  • Cloud Experience: Experience with AWS, Azure or GCP
  • 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, we want 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. We care as much about whether a model is right as whether it runs, so careful evaluation is a big part of the job. You will collaborate closely with data engineering, analytics, and cloud teams to deliver transformative AI capabilities.

Key Responsibilities

Build AI solutions:

  • Build LLM and agent-based applications (RAG, tool use, multi-step workflows) for client projects and internal products.
  • Take them from prototype to production on AWS or Azure (e.g. SageMaker, Azure ML, Databricks, OpenAI integrations), then monitor and improve them once they are live.
  • Explore and understand client data before building anything.
  • Design evaluation for LLM and agent systems: test sets, metrics, experiments and statistical comparison of approaches.
  • Decide when classical ML, forecasting or optimisation fits better than an LLM, and combine them where it helps (e.g. an agent that draws on a demand forecast).

Collaboration & Stakeholder Management:

  • Work with engineers, data scientists and client teams to understand problems and shape solutions.
  • Explain AI concepts clearly to technical and non-technical audiences.
  • Support business development through demos, proof-of-concept work, and technical pre-sales activities.

Share and grow:

  • Help develop our practices in LLMOps, evaluation and agent design.
  • If you would like to, write blogs, give talks or contribute to open source. We'll support you, but it's optional.

Technical Skills & Experience

Many great people hold back from applying unless they tick every box. Please don't. If you have most of the core skills and want to learn the rest, we'd like to hear from you.

Core:

  • Agentic AI & LLMs: Hands-on experience building applications on top of LLMs, including agent-based workflows (e.g. RAG, tool use).
  • Cloud AI (AWS/Azure/GCP): 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.

Nice to have:

  • Time-series forecasting or optimisation, ideally in energy
  • Vector databases and lakehouse architectures
  • Terraform, Docker/Kubernetes, or streaming (Kafka, Kinesis, Event Hubs)
  • Cloud certifications
  • Consulting or energy-sector experience

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 Senior.
  • 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.

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