15 of 15 Retrieval-Augmented Generation Jobs in Manchester

AI expert ready to take your skills to the next level? Do words like Azure OpenAI, Cognitive Services, prompt engineering, Retrieval-Augmented Generation (RAG) architectures, vector stores, and API integrations make you light up inside? If so, we want to hear from you! At 1st Central , we're on an exciting journey with AI … develop AI and Generative AI solutions using services like Azure OpenAI and Azure Cognitive Services Implement prompt engineering techniques and Retrieval-Augmented Generation (RAG) architectures. Ensure scalability, security, auditability, and efficiency of AI solutions through detailed system design and development practices. Deploy and manage AI solutions via CI/CD pipelines in Azure DevOps … deploying, and managing production-grade AI and Generative AI systems. Extensive experience with Cloud-based AI and Cognitive Services, and Retrieval-Augmented Generation (RAG) architectures. Deep expertise in API integration, preferably within the Azure ecosystem. Experience with Infrastructure as Code (IaC) across development, testing, and production environments. Solid understanding of Azure networking principles, security More ❯

applications using modern full-stack technologies. Key Responsibilities Develop and deploy AI-powered features into production systems. Build and optimise automation pipelines for scalable delivery. Implement RAG (Retrieval-Augmented Generation) and reasoning models into customer-facing applications. Collaborate with data scientists and engineers to translate technical concepts into real-world tools. Build full-stack … MERN Stack (MongoDB, Express.js, React.js, Node.js or similar..) Strong proficiency in Python, particularly for AI and ML applications. Hands-on experience with PyTorch, GenAI, and reasoning models. Experience implementing RAG architectures in production environments. Solid understanding of both SQL and NoSQL databases Experience building and consuming APIs; familiarity with CI/CD practices. Nice to Have Exposure to cloud platforms More ❯

applications using modern full-stack technologies. Key Responsibilities Develop and deploy AI-powered features into production systems. Build and optimise automation pipelines for scalable delivery. Implement RAG (Retrieval-Augmented Generation) and reasoning models into customer-facing applications. Collaborate with data scientists and engineers to translate technical concepts into real-world tools. Build full-stack … MERN Stack (MongoDB, Express.js, React.js, Node.js or similar..) Strong proficiency in Python, particularly for AI and ML applications. Hands-on experience with PyTorch, GenAI, and reasoning models. Experience implementing RAG architectures in production environments. Solid understanding of both SQL and NoSQL databases Experience building and consuming APIs; familiarity with CI/CD practices. Nice to Have Exposure to cloud platforms More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

love of clarity is critical. We're also entering an exciting new phase of AI-powered innovation across the platform. Experience with AI product workflows, especially Agentic AI Architecture, RAG, CAG, and LLM prompting, will be considered a strong bonus. Core Responsibilities Run Agile Ceremonies Lead daily standups, sprint planning, sprint reviews, and retrospectives Ensure agile cadence and rituals run More ❯

work on fine-tuning, integrating, and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to More ❯

work on fine-tuning, integrating, and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to More ❯

work on fine-tuning, integrating, and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to More ❯

work on fine-tuning, integrating, and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to More ❯

work on fine-tuning, integrating, and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to More ❯

articulate problems and construct solutions. BCS Practitioner Certificate in Enterprise and Solutions Architecture BS in Information Systems, Engineering, or related field Foundational understanding of AI concepts, including prompt engineering, RAG, and LLMs Enterprise Architecture best practices and tools (e.g., LeanIX) Experience working in a startup environment or similar Knowledge of Construction technologies and products, such as BIM, data collection apps More ❯

to help design and deploy LLM-powered tools. You'll prototype and build end-to-end systems that extract and act on data from diverse document types using LLMs, RAG pipelines, and agent-based reasoning. Experience Required Strong Python engineering skills and experience with AI tooling Experience building and deploying microservices (FastAPI, Kubernetes) Familiarity with CI/CD pipelines and … modern software practices Background in document extraction and reasoning (LLMs, RAG, agents) Exposure to multimodal data (text, images, structured) MLOps, model monitoring, and rapid prototyping (Streamlit, Dash, etc.) 3–4+ years in software or ML engineering roles The Interview Process Intro chat with Lead Data Scientist Take-home technical challenge – Focused on structuring AI software Final stage interview – Deep dive More ❯