4 of 4 Retrieval-Augmented Generation Jobs in Merseyside

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

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 ❯

trajectory forecasted for 2025. Our platform is already trusted by nearly half of the top 50 financial institutions globally. The Technology At the core of our platform is a RAG-based architecture, purpose-built for finance. It’s designed to replicate the kind of deep reasoning you see in leading models like OpenAI’s o1—capable of parsing nuanced questions … frameworks (Python, React, TypeScript, etc.) and can build production-ready systems end-to-end Are excited by AI-native product design and have experience working with or around LLMs, RAG, or agent-like architectures Love talking to users and solving real business problems—not just coding in a vacuum Bring an entrepreneurial mindset, with an eye toward growing into a More ❯