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Octopus was founded with a mission to use technology to accelerate us towards a low-carbon future. That’s why we created Kraken - our own technology platform from scratch which now serves over 70 million households and is a core reason why Octopus is the number one energy supplier in the UK. We do it by hiring clever, curious, and self-driven people, enabling them with modern tools and infrastructure and giving them lots of autonomy. We have been using GenAI in live, customer-facing environments since 2022, including one system that creates tens of thousands of high-quality emails for our Energy Specialists, combining our deep knowledge of the energy industry and the Octo communication style with customer-specific data from Kraken. We are now looking for an AI Engineer. You will take ownership of building and scaling the systems that allow Octopus teams to use Generative AI models (LLMs, RAG, agents). This may take the form of a platform, shared services, or other scalable infrastructure. You will work with a cross-functional team to build out flagship AI projects and will continue to develop the best practice for how we work with AI at Octopus. You’ll report into the Head of AI and work on developing solutions that genuinely move us closer to Net Zero in a company passionate about building great technology to change the way customers use energy. You’ll have wide open problems to solve, so you’ll need to be comfortable with ambiguity, figuring out an approach and validating it fast.
Job Responsibility:
Design and Develop AI Platform Services - Build reusable, scalable services that expose GenAI models, knowledge retrieval pipelines, and agent workflows to application teams
Knowledge Base Development - Build and maintain knowledge retrieval systems including embedding generation, chunking, and strategies for database management
AI Ops, evals and observability - Setting up frameworks for monitoring and evaluating AI output quality (relevance, accuracy, safety, drift, cost) and platform observability (latency, cost, usage)
Context Engineering - Design systems for prompt assembly: Create prompt templates, system prompts and guidelines for platform users
Requirements:
Deep Understanding of GenAI - experience working with LLMs
Data Product Development - experience building Python-based applications and/or data products, with hands-on work in data-intensive and machine learning systems
AI model evaluation and observability - Experience of different ways of evaluating AI models and applications. Implementing logging, tracing, and monitoring in systems
Context Engineering and Knowledge Grounding - Experience of optimising and grounding GenAI models and applications through prompt design, RAG and knowledge base integration
Software Development Practices - Strong grounding in Git, testing, CI/CD frameworks
Ability to thrive in a fast moving environment - Dealing with ambiguity, setting clear priorities, and translating ideas into actionable plans
Nice to have:
GenAI Application Development - Hands-on experience of building AI applications
Experience of using techniques such as tool calling, agentic workflows, finetuning and reinforcement learning
Backend Development - Experience designing and building applications and services that encapsulate business logic, data and information flows and connect AI models into wider applications
Pipeline Design and Build - Experience integrating different types of databases and building and optimising data pipelines