1 to 25 of 27 Permanent Retrieval-Augmented Generation Jobs in the South East

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 ...

unstructured data and build reliable data and development pipelines. Vector Databases & Services - Hands‐on experience using vector databases and retrieval‐augmented generation (RAG) to store and retrieve data for LLMs. Skills Development for LLM Models - Understanding of selected LLM technologies and the ability ...

including AI and ML services Solid foundation in software engineering principles for scalable, production-grade systems Experience designing and delivering enterprise AI solutions including RAG-based architectures using vector databases Proven experience delivering full-stack AI or ML systems within enterprise environments Strong understanding of advanced agent architectures, reasoning systems ...

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 ...

Experience: Proven track record of leading the delivery and maintenance of complex software products in a DevOps and Agile environment. Deep practical experience with RAG, LLMs, and MLOps. Expert‐level understanding of NLP, Embeddings, and Prompt Engineering. Expert proficiency in Python, React, and TypeScript. Significant experience with Cloud platforms ...

minimum of 10 years of experience in software engineering with a proven track record in a DevSecOps and Agile environment. Deep practical experience with RAG, LLMs, and MLOps, alongside expert proficiency in Python, React, and TypeScript. What will give you a competitive edge (preferred qualifications): Expert-level understanding ...

minimum of 10 years of experience in software engineering with a proven track record in a DevSecOps and Agile environment.* Deep practical experience with RAG, LLMs, and MLOps, alongside expert proficiency in Python, React, and TypeScript.**What will give you a competitive edge (preferred qualifications):*** Expert-level understanding ...

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 ...

senior client stakeholders to shape and deliver solutions Build and deploy end-to-end AI products , not just prototypes Apply technologies such as LLMs, RAG systems, and agentic workflows to real business problems Translate technical outputs into clear commercial value and measurable impact Mentor and develop consultants, helping build capability ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

leading a team of engineers, defining technical roadmaps, and maintaining high engineering standards. Ideal candidates should have extensive experience with AI technologies, especially in RAG and LLMs, as well as strong programming skills in Python, React, and TypeScript. The position is hybrid, requiring three days in the office weekly, located ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation → prototyping → production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

value What We’re Looking For Experience delivering AI/GenAI solutions in a real business environment (not just demos) Strong understanding of LLMs, RAG, and workflow/automation patterns Ability to turn ambiguous problems into practical, shipped solutions Hands-on builder—comfortable prototyping, integrating tools, and iterating quickly Outcome ...

working in regulated environments or with legal workflows. Thorough understanding of AI technologies used in legal environments (LLMs, NLP, extraction/classification models, summarisation, RAG pipelines, etc.) Hybrid Working Birketts is a flexible business which has embraced a hybrid working model where our colleagues enjoy a mix of home ...

involved across the full lifecycle of GenAI products—from proof of concept through to scalable production deployment and monitoring. Working with modern LLMs, RAG architectures and agentic frameworks , you’ll collaborate with data engineers, solution architects and business stakeholders to deliver secure, cost-effective AI solutions on AWS. Key Responsibilities … Design and implement production GenAI applications using LLMs (e.g. Anthropic, AWS Bedrock models) Build and deploy RAG systems using vector databases and semantic search Develop agentic AI workflows using frameworks such as LangChain, LangGraph, CrewAI or similar Create effective prompt engineering strategies with appropriate guardrails for production systems Implement monitoring ...

/or data platforms at scale Experience in a senior technical or leadership role (3+ years) Strong understanding of modern AI approaches, including LLMs, RAG, and orchestration Knowledge of cloud environments, security, and compliance considerations Morgan Hunt is a multi-award-winning recruitment business for interim, contract and temporary recruitment ...

ideas to both technical and non‐technical audiences Nice to Have Research experience in areas such as large language models (e.g., training, fine‐tuning, RAG, evaluation, synthetic data generation), agentic AI, big data technologies, or digital twins Familiarity with emerging research and tooling ecosystems such as LangChain, LangGraph ...