18 of 18 Permanent Retrieval-Augmented Generation Jobs in the North of England

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 ❯

using python and SQL The ability to build AI-driven solutions using large language models (LLMs), and techniques such as retrieval-augmented generation (RAG) and agent-based approaches, supported by frameworks like LangChain and CrewAI Skills in leveraging AI model APIs (e.g. OpenAI, Anthropic) and rapid prototyping tools such as Streamlit, along with AI More ❯

and services so they can create brilliant technology solutions for UK businesses. We're seeking a Senior Software developer, to work predominantly on the adoption of AI & code generation tooling across a wide range ofproducts. This role is a hybrid role with the expectation of minimum 1-2 days per week, working from our Hessle office (nr Hull … Doing Analyse business requirements and translate them into intelligent, scalable solutions using C# and .NET. Design and implement AI-powered developer tools and services, including prompt-based code generation, smart documentation, and internal copilots. Own the end-to-end integration of LLMs and AI APIs (e.g., OpenAI, Azure OpenAI), including prompt design, evaluation, and optimization-without relying on … environment. Nice to Have: Experience with LangChain, Semantic Kernel , or similar orchestration frameworks. Familiarity with embedding models , vector stores, or retrieval-augmented generation (RAG). Contributions to open-source AI/dev tools or participation in AI hackathons. What's in it for you? Remote/Hybrid working The chance to work in a 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 ❯

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 ❯

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 ❯

of emerging AI tools YOUR SKILLS AND EXPERIENCE: A successful AI Engineer will have the following skills and experience: Strong Python with AI/ML exposure Experience with LLMs, RAG pipelines, document extraction Prior work with multimodal data (NLP, CV, tabular) THE BENEFITS: You will receive a salary, dependent on experience. Salary is up to £70,000 On top of More ❯

be necessary for meetings, but these will be fully funded. As a GenAI/Python Engineer , you’ll help design, build, and scale AI-powered applications using tools like RAG, intelligent agents, and LLMs. This hands-on, client-facing role blends technical depth with consulting impact. You will: Develop and scale GenAI solutions from POCs to production. Lead development across More ❯

What You Will Do Design and implement scalable ML and deep learning models using PyTorch, TensorFlow, Scikit-learn, and other modern frameworks. Build and optimize RAG pipelines using models like GPT, Claude, or other LLMs integrated with document retrieval systems. Develop production-ready ML applications in cloud environments (AWS, SageMaker, Databricks, etc.). Leverage GPU-based computing resources … and ensure model fairness, interpretability, and compliance. Explore and integrate open-source LLM models and fine-tuning strategies for specialized domain applications. Drive innovation in prompt engineering and generation pipelines for AI-assisted systems. Skills and Experience You Will Need Required: 5+ years of hands-on experience in machine learning, with at least 2 years focused on deep … knowledge of machine learning algorithms, statistics, and deep learning. Proven experience with PyTorch, TensorFlow, Scikit-learn, and transformer-based models. Practical knowledge of LLM integration (e.g., GPT, Claude) and RAG architecture. Experience with ML lifecycle management tools like AWS SageMaker, MLflow, or Databricks. Working knowledge of CUDA, Nvidia GPUs, and distributed training. Experience with AWS services (S3, Lambda, EC2, SageMaker More ❯

standards are met. Implement best practices for MLOps, including CI/CD, automated testing, model versioning, and data validation. Architect integrations with vector databases, cloud platforms, and retrieval-augmented generation systems. Qualifications: 3+ years of experience delivering full-stack AI/ML applications in production. Proficiency in Python, especially with ML libraries such as More ❯

technologies An analytical mind able to visualise, conceptualise, articulate problems and construct solutions. BCS Practitioner Certificate in Enterprise and Solutions Architecture 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 ❯

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 More ❯