1 to 25 of 48 Remote Retrieval-Augmented Generation Jobs in East Anglia

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. C Contribute to the design and development of full-stack applications More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

cross-functional teams. Experimental Approach: A passion for rapid iteration, learning from experiments, and data-driven development. Preferred Qualifications Experience designing RAG (Retrieval-Augmented Generation) or CAG systems using vector databases and hybrid retrieval techniques. Knowledge of LLM security challenges, including prompt More ❯

in production. Strong background in LLMs, NLP, and conversational AI (e.g., GPT, Claude, Mistral, LLaMA, etc.) Expertise with frameworks like LangChain, Hugging Face, OpenAI, RAG pipelines, and vector databases (e.g., Weaviate, Pinecone, Chroma) Solid knowledge of AI system architecture, including model serving, monitoring, and optimization Strong programming skills in Python More ❯