1 to 25 of 49 Retrieval-Augmented Generation Jobs in the South East

Machine Learning solutions into production environments Build and optimise Generative AI applications using Large Language Models (LLMs) Develop Retrieval Augmented Generation (RAG) architectures and AI agents Build speech-to-text, voice AI and accessibility-focused solutions Create intelligent chatbots, virtual assistants and conversational … Learning Commercial experience developing Machine Learning solutions Experience working with Large Language Models (LLMs) Knowledge of Generative AI technologies RAG (Retrieval Augmented Generation) implementation experience NLP and Conversational AI development Model training, testing and evaluation Prompt Engineering and AI workflow optimisation Programming & Frameworks Strong ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

hands-on with Python and Azure , possessing a proven track record of designing and deploying Retrieval-Augmented Generation (RAG) patterns. Key Responsibilities Architect & Govern: Design and implement robust on-premises, cloud, and hybrid AI solutions, ensuring strict technical governance and best practices across … client relationships to secure consensus on complex technical architectures. Essential Skills Clearance: Active UK Government Security Check (SC). GenAI: Expertise in LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks ...

deploy Generative AI systems end to end – from use-case discovery through to production Build Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, and enterprise data sources Develop agent-based workflows, orchestration layers, and multi-step reasoning systems Work extensively with open-source … experience building and deploying Generative AI or applied ML systems Strong hands-on engineering background – you actively code, build, test, and deploy Experience with: RAG architectures and vector databases Hugging Face models, pipelines, and inference tooling Prompt engineering and LLM evaluation techniques Agentic systems and orchestration patterns Cloud-native experience ...

intelligent, distributed systems Architecting and building AI-driven capabilities , including: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) AI agents and orchestration workflows Designing and maintaining backend services and APIs (primarily Python, FastAPI/Flask) Building and scaling data pipelines and search systems using … Python or similar languages Significant experience with backend development , microservices, and distributed systems Hands-on experience with AI/ML systems , including LLMs, RAG, or vector databases Solid understanding of containerisation and orchestration (Docker/Kubernetes) Experience delivering secure, on-premise or regulated systems Proven ability to lead technically , mentor ...

implementing AI agents, automation workflows and AI-powered business solutions. Strong understanding of prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling and LLM integrations. Excellent business analysis, stakeholder management and workshop facilitation skills. Ability to translate complex business requirements into practical, scalable ...

experience using frameworks such as PyTorch, TensorFlow, LangChain, or similar. * Experience working with LLMs, prompt engineering, vector databases, and retrieval augmented generation (RAG) architectures. * Strong knowledge of cloud platforms including AWS or Azure. * Experience with data engineering and orchestration tools such as Databricks, Airflow ...

from project start. Solutions may use a wide range of approaches, including agentic systems, model context protocol (MCP) servers, retrieval augmented generation (RAG), classical machine learning, knowledge graphs, data transformations, and business logic. The emphasis is on solving the right problem rather than using … data science libraries such as pandas, scikit-learn, LangChain, etc. • Experience with LLMs, including foundation model APIs, prompt engineering, and retrieval augmented generation • Substantial depth and breadth across state-of-the-art AI/ML techniques such as Generative AI, NLP, and Deep Learning ...

core data science libraries such as pandas, scikit-learn, LangChain, etc. Experience with LLMs, e.g. foundation model APIs, prompt engineering, retrieval augmented generation Substantial depth and breadth across state-of-the-art AI/ML techniques such as Generative AI, NLP, Deep Learning, etc. ...

outcomes. The Role and Deliverables Design, build, and maintain production-grade AI-enabled applications consuming standardised GenAI services such as LLM-based capabilities, RAG, summarisation, and classification. Develop cloud-native microservice or full-stack applications using API-led architectures. Integrate AI capabilities securely and reliably, in line with platform standards ...

Architect Professional* Databricks Certifications* CDMP Certification* Terraform, Bicep or Infrastructure-as-Code experience* dbt, Airflow or modern orchestration frameworks* Experience supporting AI, GenAI or RAG-based architectures* Experience within Financial Services, Healthcare, Life Sciences, Energy, Retail or Manufacturing Robert Half Ltd acts as an employment business for temporary positions ...

Familiarity with AI-assisted development tools such as GitHub Copilot, Claude, and Cursor. Azure AI Engineer certification or equivalent practical experience. Understanding of MCP, RAG architectures, or vector databases. Benefits Industry-leading training and development opportunities. Competitive salary with progression potential. 23-days holiday allowance, increasing with length of service ...

Azure AI, GenAI and Agents. Experience with digital assistant solutions enhanced by AI such as Microsoft Copilot. Demonstrable experience with Retrieval-Augmented Generation. Solid understanding of MLOps. Ability to engage confidently with clients and present technical solutions clearly. It would be great if you had: Experience ...

deployment and monitoring. Lead end to end delivery of AI and LLM based solutions across underwriting and insurance journeys Build and deploy agentic workflows, RAG pipelines, and LLM powered applications for internal and customer facing products Work across structured and unstructured data, collaborating closely with Machine Learning and GenAI specialists ...

Requirements AWS Bedrock Expert: Hands-on experience with Bedrock Agents, Knowledge Bases, and model lifecycle management. AI/ML Depth: Strong grasp of FMs, RAG, tool-use, and the failure modes of agentic workflows. Security & Compliance: Deep knowledge of NIST AI RMF, OWASP LLM Top 10, and UK/ ...