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# Machine Learning Engineer (Generative AI, RAG & Conversational Systems)

- **Company:** Sky UK
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Python (Programming Language), Machine Learning, Scrum Methodology, Cloud Services, Search Technologies, Systems Architecture, Unstructured Data, Large Language Models, Model Validation, Generative AI, AWS Lambda, Material UI, Deployment Automation, Front End Software Development, Serverless Computing
- **Published:** June 7, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=9119eb09d78f62b7

## About the Role

Do you have experience in Python?, * E xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production

 * Solid understanding of:

 * Large Language Models (LLMs)

 * Vector databases and semantic search

 * Retrieval-Augmented Generation (RAG)

 * Robust programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset

 * Hands-on use of serverless cloud services (e.g. AWS Lambda best practices)

 * Strong team player and communicator with experience in agile, fast paced environments

Desirable"skills and"experience: " "

 * Extensive machine learning research background

## Description

We're the Global Product. We're the team behind your favourite Sky products, and the platforms that power them. We make every moment magical, everywhere. Our team is made up of self-motivated, big thinkers who have a knack for solving problems and find new ways to captivate millions of customers by putting them at the heart of everything we do. From Sky Glass, Sky Q, Peacock and NOW to news and sports apps, we make entertainment even better, and we can't wait to get started on what's next.

What you'll do

 * Design, develop and optimise GenAI-powered search and conversational chatbot experiences that integrate seamlessly into scalable mobile and TV applications used by end users

 * Build retrieval-augmented generation (RAG) pipelines combining LLMs with structured and unstructured data

 * Optimise relevance, latency, and response quality for interactive UI components

 * Evaluate and improve models using real-world feedback, analytics and experimentation

 * Collaborates closely with frontend, product, and UX teams to embed AI components into user-facing applications

 * Contributes to system architecture, model selection, and deployment strategies

 * Participates in Scrum / agile process


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