Application Guide

How to Apply for AI/ ML Engineer

at Octopus Energy

🏢 About Octopus Energy

Octopus Energy is revolutionizing the energy sector by combining AI, renewable energy, and transparent pricing to accelerate the transition to a low-carbon future. Unlike traditional utilities, they leverage technology to optimize energy distribution and customer experience, making them a purpose-driven tech company in the energy space.

About This Role

This AI/ML Engineer role focuses on building the core AI platform that powers Octopus Energy's applications, including GenAI services, knowledge retrieval systems, and AI observability frameworks. You'll directly impact how the company uses AI to optimize renewable energy distribution and enhance customer interactions through scalable, reusable AI services.

💡 A Day in the Life

A typical day might involve designing a new prompt template for an energy usage chatbot, optimizing a knowledge retrieval pipeline for customer data, and reviewing AI model evaluation metrics to ensure output quality and cost-efficiency. You'll collaborate with application teams to integrate AI services and troubleshoot observability alerts for platform performance.

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Interview Prep

Practice questions for this role

🎯 Who Octopus Energy Is Looking For

  • Has hands-on experience designing and deploying production GenAI systems (e.g., with OpenAI, Anthropic, or open-source LLMs) and understands retrieval-augmented generation (RAG) pipelines.
  • Demonstrates proficiency in Python-based data product development, with experience in embedding generation, vector databases, and chunking strategies for knowledge bases.
  • Has implemented AI evaluation frameworks (e.g., for relevance, accuracy, safety) and observability tools (logging, tracing, monitoring) in real-world applications.
  • Understands context engineering principles, such as prompt templating, system prompt design, and optimizing AI workflows for specific use cases.

📝 Tips for Applying to Octopus Energy

1

Highlight specific examples of building reusable AI services or platforms (not just one-off models) in your resume, emphasizing scalability and integration with application teams.

2

Tailor your experience to mention knowledge retrieval systems—detail your work with embedding models, vector databases (e.g., Pinecone, Weaviate), and chunking strategies.

3

Discuss AI evaluation and observability projects, quantifying outcomes like reduced latency, improved accuracy, or cost savings in AI deployments.

4

Research Octopus Energy's AI initiatives (e.g., their Kraken platform) and mention how your skills align with their focus on renewables and low CO2 goals.

5

Avoid generic AI/ML terms; use specific keywords from the job description like 'GenAI models,' 'agent workflows,' 'prompt assembly,' and 'AI Ops.'

✉️ What to Emphasize in Your Cover Letter

['Explain how your experience with GenAI and knowledge retrieval systems can help Octopus Energy build scalable AI services for their renewable energy applications.', 'Describe a past project where you implemented AI evaluation or observability frameworks, linking it to their need for monitoring output quality and platform performance.', 'Highlight your ability to design reusable AI services and collaborate with application teams, emphasizing alignment with their transparent, tech-driven mission.', 'Mention your interest in their low CO2 future goal and how AI can optimize energy distribution or customer engagement in that context.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Explore Octopus Energy's Kraken platform and their public AI/tech blog posts to understand their current AI initiatives and tech stack.
  • → Look into their renewable energy projects and how they use data/AI for grid optimization, customer billing, or CO2 reduction.
  • → Review their company values and mission around transparency and low-carbon future to tailor your application to their culture.
  • → Check their engineering team's public talks or GitHub repositories for insights into their development practices and tools.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through your experience designing and deploying a GenAI service or RAG pipeline, including challenges with scalability, latency, or cost optimization.
2 How would you build a knowledge retrieval system for energy-related data? Discuss embedding models, chunking strategies, and database management.
3 Describe your approach to evaluating AI model outputs for relevance, accuracy, and safety in a production environment. What metrics and tools would you use?
4 Explain context engineering: how do you design prompt templates and system prompts for diverse use cases, and ensure consistency across platform users?
5 How would you implement observability (logging, tracing, monitoring) for an AI platform to track usage, cost, and performance drift over time?
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⚠️ Common Mistakes to Avoid

  • Focusing only on traditional ML models without showcasing GenAI, knowledge retrieval, or AI ops experience relevant to this role.
  • Providing vague descriptions of past projects; instead, quantify outcomes (e.g., 'improved retrieval accuracy by 20%' or 'reduced AI service latency by 30%').
  • Neglecting to mention collaboration with application teams or experience in building reusable services, as this role emphasizes platform development.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Octopus Energy !

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