1 to 25 of 31 Retrieval-Augmented Generation Jobs in the South East

ready enterprise data platforms. Experience with: Machine Learning pipelines and MLOps o Generative AI and Retrieval-Augmented Generation (RAG) Vector search and unstructured data analytics o Feature stores and model governance Familiarity with Azure OpenAI, Azure AI services, and responsible AI principles . Data ...

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 ...

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 ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

discuss flexible working! Top 3 skills needed for this role: Expert AI/ML Skills – Strong in both traditional ML and modern LLM/RAG/agentic workflows Production Deployment & MLOps – Comfortable shipping scalable AI systems using APIs, microservices, Docker/Kubernetes, and cloud (GCP ideal) Business Partnering – Able … based applications. The role involves building internal and external AI solutions such as Retrieval-Augmented Generation (RAG) systems, chatbots, and voice-based customer service platforms, all deployed on Google Cloud Platform (GCP). You will work closely with data scientists, software engineers, and product ...

scaling Support experimentation, benchmarking, and cost/performance analysis of AI models Contribute to retrieval-augmented generation (RAG) solutions and data integration patterns Establish reusable API standards, frameworks, and documentation to support wider adoption Troubleshoot and optimise distributed systems and cloud-based services Core ...

party vendors. Knowledge of OO programming and software design, i.e., SOLID principles. Knowledge and working experience of AGILE methodologies. Familiarity with Databricks, and RAG application architecture a plus Experience with latency optimisation and quantisation preferred Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience ...

foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within ...

ideal for someone who excels at technical storytelling and enjoys translating complex concepts-such as retrieval-augmented generation (RAG), vector search, and large language model (LLM) integrations-into engaging, developer-focused multimedia content. You will play a key role in shaping video strategy, producing … curate a content calendar with a strong focus on video and demo assets Script, produce, and edit technical videos (e.g. step-by-step RAG demos, vector search walkthroughs, LLM integration tutorials) using tools such as Camtasia or similar Collaborate closely with engineers, developer advocates, and demo creators to ensure technical ...

across the digital twin pipelines Nice to have: Building agents/multi-agent systems Developing Retrieval-Augmented Generation (RAG) systems Agentic frameworks: LangGraph/LangChain Deployed LLMs at enterprise scale Modis International Ltd acts as an employment agency for permanent recruitment and an employment ...

mentor junior members on modern security principles. Collaboration: Work closely with AI Engineers and Data Scientists to integrate security into the LLM lifecycle (RAG pipelines, fine-tuning, and inference). Randstad Technologies is acting as an Employment Business in relation to this vacancy. ...

Develop production-ready AI agents on GCP, including secure API integrations. Integrate agent capabilities into applications using MCP servers and supporting tools. Develop reliable RAG pipelines, including ingestion, chunking, metadata and vector search. Required: Strong Python skills for backend, APIs, testing and async patterns. Practical experience with LLMs, embeddings, prompt ...

solutions aligned to client business goals. Build scalable architectures using modern cloud platforms ( Azure, AWS or GCP ). Lead the design of RAG-based solutions, AI assistants, and autonomous agent systems . Technical Leadership Guide the end-to-end delivery of complex AI implementations. Evaluate and select models, frameworks … capability. Key Skills & Experience Proven experience architecting Generative AI, Agentic AI, machine learning and automation solutions in enterprise environments. Strong expertise in LLMs and RAG architectures , including models such as OpenAI, LLaMA, Mistral or Claude. Hands-on experience with GenAI frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI or LangGraph . ...

hosting large foundation models. Experience with large language model architectures such as transformers, GANs, and VAEs, along with fine-tuning techniques, retrieval-augmented generation, contextual embeddings, vector databases, and semantic search tools Experience using deep learning frameworks and tools such as PyTorch, TensorFlow ...

including APIs, services, and user-facing interfaces. Develop GenAI, Agentic AI and machine learning solutions that are scalable and production-ready. Design and implement RAG pipelines, LLM-powered applications and intelligent agents . Integrate AI solutions with enterprise platforms and cloud environments . Collaborate with data scientists, engineers and business … tuning and evaluation. Hands-on experience with GenAI frameworks such as LangChain, LlamaIndex and agent frameworks like AutoGen, CrewAI or LangGraph . Experience building RAG architectures with vector databases (Pinecone, Weaviate, FAISS). Strong understanding of MLOps/LLMOps (MLflow, Kubeflow, Docker, Kubernetes). Experience deploying AI solutions ...