Octopus was founded with a mission to use technology to accelerate the world towards a low‑carbon future. That’s why we created Kraken – our own technology platform, which now serves over 70 million households and is a core reason why Octopus is the number one energy supplier in the UK.
We have been using GenAI in live, customer‑facing environments since 2022, including a system that creates tens of thousands of high‑quality emails for our Energy Specialists by combining deep knowledge of the energy industry with customer‑specific data from Kraken.
We are now looking for a Senior AI Engineer. You will build and scale the systems that allow Octopus teams to use Generative AI models (LLMs, RAG, agents). You will be hands‑on, working with a cross‑functional team to build out flagship AI projects and the platforms that enable others to succeed with Generative AI.
What you’ll do
- 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, process heterogeneous documents like PDFs, and build embedding pipelines that extract, chunk and embed into embedding models.
- AI Readiness: Process content from unstructured documents and make it AI‑ready: optimize structure, clear up text ambiguity, and embed rich machine‑readable metadata.
- AI Ops, evals and observability: Set up a framework for monitoring and evaluating AI output quality (relevance, accuracy, safety, drift, cost) and platform observability (latency, cost, usage).
- Centre of Excellence: Act as a centre of excellence for the whole business in the technical side of AI and LLMs usage, setting best practices and accelerating adoption.
- Governance and Guardrails: Create governance layers for implementing PII redaction, prompt filtering etc.
What you’ll need
- Deep GenAI & RAG Expertise: 2+ years of hands‑on experience building and productionizing LLM applications, with deep technical knowledge of vector databases (e.g., Qdrant, Weaviate, Pinecone, pgvector) and orchestration layers (e.g., LangGraph, LlamaIndex). Comfortable working with heterogeneous documents like PDFs. Experience with re‑ranking models.
- Production‑Grade Asynchronous Python: Strong software engineering fundamentals with deep experience in asynchronous programming (asyncio, FastAPI) to handle highly concurrent, I/O‑bound LLM and database calls.
- Advanced Context Engineering: Proven experience solving production RAG challenges such as context window management, metadata filtering and semantic routing. Experience leveraging coding harnesses like Claude code.
- Modern Software Practices: Mastery of Git, automated testing and CI/CD pipelines. Experience working with cloud platforms like AWS.
- Comfort with Ambiguity: Ability to rapidly prototype an approach, validate it with metrics, and pivot fast in a fast‑moving environment.
It would be great if you had
- GraphRAG Experience: Familiarity with Knowledge Graphs (e.g., Neo4j) and ontologies to enhance standard vector RAG with highly structured, interconnected corporate data.
- Local/Open‑Weight Model Deployment: Experience deploying and fine‑tuning open‑weight models (like Llama 3 or Mistral) via frameworks such as vLLM or Ollama to optimize token costs and privacy.
- Prompt Caching & Cost Optimisation: Practical experience implementing semantic caching layers to drastically reduce LLM API billing and response latency.
We are an equal‑opportunity employer. We do not discriminate on the basis of any protected attribute and are committed to providing equal opportunities, an inclusive work environment, and fairness for everyone.
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