8 of 8 Retrieval-Augmented Generation Jobs in Manchester

Prompt-Savvy : Comfortable with prompt engineering, model evaluation, and structured analysis of generative systems. Experimentation First : Experience (or interest) in fine-tuning, instruction tuning, RAG architectures, or guardrail implementation. Collaborative Communicator : Able to bridge the gap between research and product, working across teams to translate ideas into features. Curious & Agile More ❯

advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms and system design. A proven track More ❯

advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms and system design. A proven track More ❯

advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms and system design. A proven track More ❯

advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms and system design. A proven track More ❯

advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms and system design. A proven track More ❯

advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms and system design. A proven track More ❯

advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms and system design. A proven track More ❯