1 to 25 of 34 Permanent Retrieval-Augmented Generation Jobs in the Thames Valley

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 ...

scale machine learning algorithms and pipelines to production using big data technologies. Develop and deploy retrieval-augmented generation (RAG) systems and LLM-based applications. Design and evaluate A/B tests and communicate results across cross-functional teams. Define, implement, and monitor key performance ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

cases. Design and optimise vector search and embedding pipelines to power semantic search, recommendation, and Retrieval-Augmented Generation (RAG) systems. Architect and implement agentic AI systems capable of planning, reasoning, tool use, and autonomous task execution. Explore new approaches in automation, search, and data ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

engineering best practices, delivery processes, and a high-performance team culture. Technical Ownership Lead architecture and development of LLM-powered and agentic applications, including RAG systems, chat/workflow agents, and domain-specific AI services. Drive technical direction with sound judgement and pragmatic decision-making. Build and deploy production-grade … systems into production. Deep expertise in: LLMs & transformer-based models Agentic frameworks (e.g., LangChain, LangGraph) Retrieval-Augmented Generation (RAG) Python Building or integrating LLMs into products or workflows Working with GenAI frameworks and tooling Designing or optimising prompt pipelines Deploying or supporting GenAI features ...

structured reasoning. GenAI Application Engineering Integrate LLMs into applications using LangChain, LlamaIndex, Haystack, AutoGen and OpenAI s Assistant API patterns. Build high-performance RAG pipelines using: hybrid search, reranking, embedding optimization, chunking strategies and evaluation harnesses. Develop APIs, microservices and serverless workflows for scalable deployment. ML/LLM Engineering Work ...

/Vertex AI, including fine-tuning, vector search, and low-latency inference. Build end-to-end LLM applications, leveraging RAG (Retrieval-Augmented Generation), agentic workflows, and prompt engineering. Implement robust evaluation frameworks to monitor LLM quality, hallucinations, token usage, and content safety. Develop … Looking For Essential 5+ years' experience in machine learning engineering or applied AI roles. Recent, demonstrable experience with LLMs, Generative AI, and/or RAG-based systems. Strong Python skills using frameworks such as PyTorch, TensorFlow, Hugging Face, or Google GenAI. Experience with vector databases and retrieval-based ...

tools, and enterprise connectors. Skills: 6–8 years in Software/application engineering, AI engineering, or applied data engineering. Strong experience with LLMs, embeddings, RAG, retrieval stacks, and vector stores. Hands-on experience in multi-agent systems, agent orchestration, or MCP-like tool patterns. Proficiency in Python, including ...

strong parent-company backing. Key Responsibilities Design and deploy AI features (intelligent search, procurement agents, predictive maintenance). Build and integrate LLM-driven agents , RAG pipelines, and predictive models. Collaborate with software engineers to embed AI securely into production. Define AI standards , select tools, and shape the technical roadmap. Opportunity ...

handling both single-step LLM tasks and complex multi-step agentic workflows. As AI Engineer you will: Architect and implement the LLM, agentic and RAG modules that power the platform's low-code environment. Build primitives, abstractions and orchestration components that allow clients to construct robust agentic workflows with deterministic ...

autonomous AI What You'll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical ...

maintain CI/CD, monitoring/alerting, performance & error budgets, and safe rollback paths. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Essential Requirements 3-6+ years building production backends/services, with shipped work ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

NoSQL databases to build scalable, event-driven systems. Design and optimise APIs that serve our mobile and web products, including partner integrations. Implement efficient RAG AI backend systems for introducing advanced LLM based features Collaborate with product managers, data engineers, and frontend developers to deliver new features end to end. ...

London Industry: Insurtech | Enterprise Technology Working Practice : Hybrid London My client are on a mission to reshape the insurance landscape with next-generation technology – and we're looking for a results-driven AI Engineer to lead and scale Generative AI initiatives. What You'll Do Design, develop … fine-tune Large Language Models (LLMs) and Generative AI solutions using frameworks like OpenAI, Anthropic Claude, Gemini, or open-source models. Build and optimise RAG pipelines, vector databases, and prompt engineering strategies for enhanced AI responses. Develop AI-powered applications integrating LLMs with APIs, agents, and workflows to solve real ...

looking for AI engineers who want to build production-ready AI solutions that tackle real-world challenges in life sciences, from RAG chatbots to agentic AI systems supporting evidence-driven projects. You will design, develop, and deploy AI systems that have measurable impact, influence patient access to treatments, and support … culture where employees can grow and make a tangible impact in healthcare. Key responsibilities Design, develop, and deploy AI systems with a focus on RAG chatbots and agentic AI. Lead real-world evidence projects, ensuring AI solutions are reliable, measurable, and impactful. Implement data pipelines, retraining workflows, and monitoring ...

this, you will work alongside the rest of the group to push the state-of-the-art and build the next generation of AI-native developer tools. The sky's the limit and you'll help us get there. Key Responsibilities Stay up to date with the state … apply it to the problem of Enterprise Vibe Coding; Make use of techniques such as In-Context Learning (Prompt Engineering & Retrieval Augmented Generation) and Tool Calling to guide Agents to perform as intended; Work in an "eval-driven development" methodology, applying evaluation techniques such ...

this, you will work alongside the rest of the group to push the state-of-the-art and build the next generation of AI-native developer tools. The sky's the limit and you'll help us get there. Key Responsibilities Stay up to date with the state … apply it to the problem of Enterprise Vibe Coding; Make use of techniques such as In-Context Learning (Prompt Engineering & Retrieval Augmented Generation) and Tool Calling to guide Agents to perform as intended; Work in an "eval-driven development" methodology, applying evaluation techniques such ...