9 of 9 Permanent Retrieval-Augmented Generation Jobs in Kent

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 … experience and in-depth understanding of: - Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices ...

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 … experience and in-depth understanding of: - Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices ...

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 … experience and in-depth understanding of: - Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices ...

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 … experience and in-depth understanding of: - Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices ...

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 … experience and in-depth understanding of: - Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices ...

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 … experience and in-depth understanding of: - Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices ...

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 … experience and in-depth understanding of: - Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...