2 of 2 Retrieval-Augmented Generation Jobs in Liverpool

multi-step reasoning pipelines, and memory management using frameworks such as LangChain, CrewAI, and Autogen. Engineer and tune prompts to enhance the performance and reliability of generative tasks. Design RAG systems using vector databases like Pinecone, Chroma, and PosgreSQL for contextual retrieval. Incorporate semantic search and embedding strategies for more relevant and grounded LLM responses. Utilize Guardrails to implement applications … React for demoable outputs. Develop robust API integrations to connect AI agents with internal and external services and data sources. Use SQL to query structured databases and integrate with RAG or knowledge ingestion pipelines. Essential Skills Proficiency in Python programming. Experience in building multi-step intelligent systems. Knowledge of machine learning and artificial intelligence. Familiarity with frameworks like LangChain and More ❯

LangChain, CrewAI, and vector databases. Core Responsibilities Build and iterate on LLM/agent-based prototypes (e.g., copilots, chatbots, A2A agents). Implement multi-step reasoning, memory modules, and RAG pipelines. Use frameworks like LangChain, LangGraph, CrewAI, and tools like Pinecone, FAISS. Optimize performance and ensure responsible AI practices. Deploy via cloud platforms (AWS Bedrock, Azure AI, Google Vertex). More ❯