1 to 25 of 42 Retrieval-Augmented Generation Jobs in the South East

cases Iteratively refine prompts to resolve failures, edge cases, and domain-specific gaps Optimize prompt structure for consistency, determinism, and accuracy Context Engineering & RAG Design and manage context pipelines for LLM applications Select, rank, and format retrieved content from knowledge bases and document stores Work closely with Retrieval … production LLM applications Strong understanding of: LLM behaviour and instruction following Context windows and tokenisation Hallucination mitigation techniques Practical experience with RAG (Retrieval-Augmented Generation) architectures Solid Python skills for scripting, testing, and automation Experience building or working with evaluation frameworks (“evals”) Systematic, metric ...

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 ...

LlamaIndex for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have) Retrieval-Augmented Generation (RAG): Experience building hybrid systems combining LLMs with enterprise data MLOps & Infrastructure MLOps: Model versioning, monitoring, logging Bias Detection & Mitigation Content Filtering & Moderation Explainability & Transparency ...

experience with: Production-level LLM applications including fine-tuning and customisation Function-calling systems or retrieval-augmented generation (RAG) architectures Full-lifecycle projects from prototyping to production Mentoring junior engineers or shaping technical direction within teams What You’ll Gain A high-impact role … with visibility and technical influence The opportunity to shape the next generation of AI platforms Hybrid working with three days per week onsite in Oxfordshire and two days remote A supportive team that values experimentation, clean engineering and continuous learning A salary ...

business requirements into technical architectures for GenAI and machine learning use cases. Implement prompt engineering, retrieval-augmented generation (RAG), and integration patterns with enterprise systems. Configure and optimise Microsoft Copilot Studio and other AI tools for internal use cases. Support the development of repeatable … Azure AI services. Strong ability to quickly prototype and test AI/ML solutions. Experience with APIs, vector databases, and integration patterns. Understanding of RAG, LLMOps, and prompt engineering. Job Offer Competitive daily rate (Inside IR35). Opportunity to work on innovative AI projects in the financial services industry. Temporary ...

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 ...

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 ...

Balancing cost, performance, and reliability Applied AI Working with Azure OpenAI APIs beyond prompt demos Retrieval-augmented generation (RAG) Embeddings, vector stores, and evaluation Guardrails, rate limiting, and failure handling Human-in-the-loop workflows Integration & automation Integrating AI into existing enterprise systems Automating ...

following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure, AWS, or GCP Benefits The successful Senior ...

pairs, decision trees, and intent-based chunks* Apply consistent taxonomy, tagging, and metadata to support retrieval-augmented generation (RAG) and AI consumption* Align knowledge with chatbot/model requirements, design prompt-ready content, analyze performance, and refine based on real user queries* Collaborate with ...

microservices or serverless architectures Practical exposure to machine learning plus generative AI models including LLMs SLMs vision models Familiarity with AI frameworks such as RAG LangChain LlamaIndex plus agentic frameworks Hands-on cloud experience within Azure or AWS plus CI/CD tooling and Git Desirable Skills ...

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 ...

/ML solutions in production Strong programming skills in Python and SQL Experience with classical machine learning and LLMs/generative AI (prompting, RAG, APIs) Solid software engineering fundamentals Experience working with cloud platforms Benefits The successful Senior AI Engineer will receive the following benefits: Salary ...

Python and JavaScript Hands-on experience with backend frameworks and modern frontend frameworks Deep knowledge of Generative AI and agent-based AI frameworks including RAG and orchestration patterns Strong experience working with vector databases and AI-specific data stores Solid understanding of AI and ML models, LLMs and generative ...

APIs, JSON and spatial query engines (e.g. Apache Spark) Experience with Generative AI technologies including Large Language Models (LLMs), Retrieval Augmented Generation (RAG) and Prompt/Context Engineering Passion for continuous improvement and delivering high-quality solutions The Rewards We want you to love ...

services • Develop applied LLM/NLP systems (conversational AI, embeddings, intent/entity recognition) • Work with PyTorch models deployed in AWS environments • Contribute to RAG, agentic workflows, and orchestration patterns • Collaborate closely with product, engineering, and leadership • Communicate technical decisions clearly to non-technical stakeholders Key details • Salary ...

have: Strong Python engineering skills for backend services and API integrations. Hands-on experience working with LLMs and related tooling, including: LLMs RAG patterns, vector databases, function calling, and prompt design. Solid experience with Google Cloud Platform, ideally including: Vertex AI, Model Garden, BigQuery, Cloud Run or Cloud Functions Ability ...