12 of 12 Retrieval-Augmented Generation Jobs in the North West

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 ❯

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 ❯

and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker and 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 ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

principles Practical MLOps experience of training and tuning deep learning models and evaluating performance (e.g working with PyTorch to build deep learning pipelines) Experience with large language models including; RAG, prompt engineering, and finetuning techniques as well as model API integration Experience across a range of deployment environments including public cloud and high-performance computing clusters Practical experience creating user More ❯