26 to 50 of 75 Retrieval-Augmented Generation Jobs in the UK excluding London

secure cloud development practices and IAM role design. Understanding of LLM fine-tuning, embeddings, vector stores (e.g., Pinecone, FAISS, OpenSearch). Exposure to contact centre automation, conversational agents, or RAG pipelines. Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities More ❯

Strong understanding of AI technologies, including machine learning, natural language processing, or computer vision; or mathematical optimization. Experience with LLMs and Generative AI techniques such as fine tuning, pruning, RAG systems, transfer learning etc. Excellent command of Python, Docker and Git. Experience with GitHub/Gitlab workflows and functionalities. Solid understanding of agile development methodologies and best practices. Excellent leadership 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 ❯

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 ❯

team building scalable GenAI solutions, architect Agentic AI components, and integrate them into enterprise systems.Skills- Strong experience in full-stack and cloud-native engineering (Azure preferred) Expertise in GenAI (RAG, prompt engineering, LLM APIs) Deep knowledge of Agentic AI systems and orchestration Proficiency in Python, SQL, NodeJS (React is a plus) Agile leadership and stakeholder collaboration skills Details- 6 month 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 ❯

Shop, Sports & Social Club and More Skills Required: Proficiency in Data Science techniques, including statistical models and ML algorithms. Expertise in NLP, with a keen understanding of LLM and RAG technologies. Strong development capabilities, particularly in Python. Experience with data exchange, processing, and storage frameworks (ETL, ESB, API, SQL, NoSQL, and Parquet). Comfort with Agile development methodologies. Excellent teamwork More ❯

models using unsupervised learning, deep learning (e.g. auto-encoders), and novelty detection techniques. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence, risk, and compliance teams to More ❯

models using unsupervised learning, deep learning (e.g. auto-encoders), and novelty detection techniques. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence, risk, and compliance teams to More ❯

deepen our understanding of AI and ML model behaviour, enabling the safe and effective exploitation of advanced AI. This includes contributing to initiatives such as LLM-as-a-judge, RAG evaluations, and agentic workflow assessment. To thrive in this role, you should possess a strong foundation in data science and a passion for responsible AI. We are looking for candidates 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 ❯

Architect multi-step agent workflows using: Semantic Kernel SDK (C# or Python) Azure OpenAI (GPT-4, function calling, chat completion) Planner and Kernel Memory APIs for reasoning and memory RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in: SharePoint, OneDrive, Teams, Outlook, and Planner Use More ❯

MVPs, then production ready services. - Develop, package and maintain shared Python libraries; ensuring robust and clean architecture, testing and CI/CD standards. - Design and deploy LLM based extraction, RAG and agentic workflows over large document collections, and drive value through integration of latest AI tech (agentic workflows etc.) - ️ Communicate clearly with technical and non technical stakeholders. Required Skills & Experience 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 ❯

optimized for complex financial workflows Event-driven distributed systems handling sensitive data at enterprise scale The Tech Stack AI/ML : Multi-agent frameworks, fine-tuning, reinforcement learning, advanced RAG Platform : AWS serverless, event-driven architecture, microservices Languages : Python, with modern ML libraries (PyTorch, TensorFlow) What They're Looking For 5+ years developing AI/ML systems beyond basic integration More ❯

learning, and contribute to a culture of innovation. AI & Cloud Expertise Familiarity with LLM evaluation techniques, output scoring, and validation frameworks. Understanding of key concepts such as prompt engineering, RAG, model orchestration, and hallucination detection. Experience in testing for accuracy, relevance, and consistency of AI model predictions/generations Defining Performance Metrics for AI services and testing for the same More ❯

SINDy) Working knowledge of cell biology. Experience with Python, C, R or related scientific computing languages. Preferred Qualifications - Experience working with causal representation learning Experience with RAG (retrieval-augmented generation) and GraphRAG a big plus Experience with building and deploying software on GitHub, PyPI, Anaconda Cloud, and Docker Hub, as well as use of 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 ❯