1 to 25 of 162 Permanent Retrieval-Augmented Generation Jobs in England

interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join … relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models … skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search More ❯

interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join … relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models … skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search More ❯

interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join … relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models … skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search More ❯

interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join … relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models … skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search More ❯

Develop prototypes and experimental models to explore novel AI-driven legal solutions. Design and implement retrieval-augmented generation (RAG) pipelines , leveraging embeddings, vector databases, and structured retrieval techniques. Optimise LLM inference and fine-tuning using techniques such as LoRA, PEFT, prompt engineering … Python, Pydantic, FastAPI, and working with LLM APIs (OpenAI, Anthropic, Mistral, etc.) . Understanding of retrieval-augmented generation (RAG), vector databases, embeddings, and structured AI retrieval . Hands-on experience with LLM-based planning, reasoning, and autonomous task execution . Familiarity with More ❯

IT Consultancy to develop and scale cutting-edge GenAI full-stack applications using technologies like Retrieval-Augmented Generation (RAG), intelligent agents, and LLMs. Salary - £55,000 per annum + Additional Benefits Remote with occasional client visits Candidates must be eligible for SC Clearance No … 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 FullStack applications for both GenAI and non-GenAI projects. More ❯

IT Consultancy to develop and scale cutting-edge GenAI full-stack applications using technologies like Retrieval-Augmented Generation (RAG), intelligent agents, and LLMs. Salary - £55,000 per annum + Additional Benefits Remote with occasional client visits Candidates must be eligible for SC Clearance No … 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 FullStack applications for both GenAI and non-GenAI projects. More ❯

agents, knowledge graphs and chatbots to enhance user experience and efficiency. Leading projects that utilize Retrieval-Augmented Generation (RAG) and GraphRAG techniques. Developing and optimizing AI models using machine learning and AI engineering platforms. Mentoring and guiding team members to foster a culture of … showcasing these solutions to clients and communicating their business impact. Experience in providing solutions using Retrieval-Augmented Generation (RAG), GraphRAG, knowledge graphs, code agents, deep research agents, and chatbots. Familiarity with AI engineering platforms (Azure AI Foundry, AWS Bedrock, GCP Vertex AI) and employee More ❯

interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join … this dynamic role varied duties will include: Search relevancy engineering.Conversational AI Development: Design, train, fine-tune, and deploy LLMs with reasoning capabilities.Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources.Model Fine-Tuning & Training: Train … or OpenSearch.Required skills & experience: 3-5+ years in machine learning and software developmentProficient in Python, PyTorch or TensorFlow or Hugging Face TransformersExperience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments.Full-stack experience (React, TypeScript, Node.js) and API development.Familiarity with vector search and multi More ❯

services You'll connect a variety of APIs (including Claude, OpenAI and others) to enable dynamic conversational agents and intelligent automation. Building Retrieval-Augmented Generation pipelines You'll design smart knowledge retrieval systems using vector databases to enhance the relevance and accuracy … API development and integration , particularly in connecting LLM APIs to external systems. Strong understanding of retrieval-augmented generation (RAG) frameworks and vector databases . Proficiency in Python for developing scalable data and AI solutions, including experience with data processing libraries, API interaction, and structuring … with Claude, AWS Redshift, or graph-based systems like Anzo. So, what's in it for you? The chance to shape the next generation of AI-powered enterprise tools with real business impact. Autonomy to experiment with new technologies and deploy scalable applications in production. Exposure to advanced More ❯

to redefine primary care while helping people live happier, healthier, and longer. Education Masters Degree About the Role You will design, build, and optimize RAG pipelines that combine large language models (LLMs) with domain-specific retrieval systems, enabling natural language understanding and reasoning over patient data, clinical guidelines … work will directly impact how patients and clinicians interact with our platform, enabling safe, accurate, and context-aware content surfacing. Responsibilities Design and implement RAG architectures using open-source and potentially proprietary LLMs (e.g., LLaMA, Mistral, OpenAI, Anthropic). Build and maintain retrieval pipelines over structured and unstructured … notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with product, clinical, and data science teams to fine-tune More ❯

agents to interact autonomously with data sources and external APIs using advanced prompt engineering and retrieval-augmented generation (RAG) Fine-tune and optimize pre-trained large language models and multi-modal models for targeted use cases, ensuring high performance and low latency in production. More ❯

and scaling GenAI models for commercial applications, such as knowledge base creation, recommendation systems, and retrieval-augmented generation (RAG). Key Responsibilities: Develop and deploy GenAI models tailored for commercial applications. Implement RAG techniques and other generative AI strategies to enhance business processes. Ensure More ❯

exciting point in our journey, leveraging Generative AI (GenAI) , Large Language Models (LLMs) , and advanced Retrieval-Augmented Generation (RAG) techniques to build intelligent, data-driven systems that deliver powerful PR insights. You'll also work on developing agentic workflows that autonomously orchestrate tasks, enabling More ❯

with experience in deploying and leading complex AI/ML workloads. Implemented production applications using Retrieval-augmented generation (RAG) concepts and managed large Knowledge Bases (KBs) with embeddings, chunking and other optimization techniques within VectorDBs. Expert programming skills in languages such as Python, Java More ❯

AI initiatives span categorization, summarization, data analysis, automated content generation, and rich media analysis - leveraging both existing AI/ML tools (LLMs, RAG, embeddings, agentic workflows) and custom solutions. You'll collaborate across teams to build practical, scalable AI solutions - where precision matters. With a "you build it … Select, train, and fine-tune AI models based on need, leveraging appropriate techniques (e.g. LLMs, Retrieval-Augmented Generation (RAG), Vector Databases, Embeddings, Multi-modal AI, Agentic Workflows) (Model Development). Integrate and deploy scalable AI based applications, backend services and APIs (Deployment). Develop More ❯

AI initiatives span categorization, summarization, data analysis, automated content generation, and rich media analysis -leveraging both existing AI/ML tools (LLMs, RAG, embeddings, agentic workflows) and custom solutions. You'll collaborate across teams to build practical, scalable AI solutions - where precision matters. With a "you build it … Select, train, and fine-tune AI models based on need, leveraging appropriate techniques (e.g. LLMs, Retrieval-Augmented Generation (RAG), Vector Databases, Embeddings, Multi-modal AI, Agentic Workflows) (Model Development). Integrate and deploy scalable AI based applications, backend services and APIs (Deployment). Develop More ❯

AI agents, leveraging LLMs, reinforcement learning, vector databases, and autonomous systems . As a key AI strategy leader , you'll define the next-generation consumer experiences , enabling real-time merchant insights , AI-powered Ops automation , and cutting-edge chatbot capabilities . You will collaborate closely with executive leadership … making, and task execution. LLM Fine-Tuning & Enhancement: Adapt foundational models like GPT, Llama, integrating retrieval-augmented generation (RAG), personalization, and continuous improvement loops. Multi-Agent Systems: Implement collaborative AI systems that solve complex challenges in payments and customer interactions. Long-Term AI Memory … Learning, or related field. Experience: 7+ years in AI/ML development, with expertise in Python, TensorFlow, and PyTorch. Core AI Knowledge: LLM Optimization (RAG, fine-tuning, prompt engineering) Vector databases for AI retrieval Multi-agent systems and reinforcement learning Cloud AI deployment (AWS, Azure), inference optimization A More ❯

NLP), Generative AI, Multimodal Large Language Model (MLLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation (RAG), Computer Vision, Responsible AI, LLM Agents, Evaluation, and Model Adaptation. Key job responsibilities As an Applied Scientist on our team, you will be responsible for … test experiments, optimize and deploy your models into production, working closely with software engineers. Establish automated processes for large-scale data analysis and generation, machine-learning model development, model validation and serving. Communicate results and insights to both technical and non-technical audiences, including through presentations and written More ❯

Technologies : Work with state-of-the-art AI techniques, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), and apply them to real-world business contexts. Cross-Functional Collaboration : Partner with business stakeholders to understand objectives and translate them into actionable machine learning More ❯

Technologies : Work with state-of-the-art AI techniques, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), and apply them to real-world business contexts. Cross-Functional Collaboration : Partner with business stakeholders to understand objectives and translate them into actionable machine learning More ❯

Technologies : Work with state-of-the-art AI techniques, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), and apply them to real-world business contexts. Cross-Functional Collaboration : Partner with business stakeholders to understand objectives and translate them into actionable machine learning More ❯

applications by setting up AI sandboxes for experimentation, deploy advanced prompt engineering techniques (such as retrieval-augmented generation RAG ), fine-tune models, automate complex workflows and monitor production AI applications. This role requires regular attendance at the NAO's office either in Victoria, London More ❯

Engineer with full-stack development experience to work on cutting-edge projects involving Generative AI , Retrieval-Augmented Generation (RAG) , and multi-agent reasoning frameworks . This is a hands-on, end-to-end engineering role with impact across the full ML lifecycle – from experimentation … to deployment. Conversational AI & Reasoning: Design, fine-tune, and deploy advanced LLMs with agentic capabilities RAG Pipelines: Build and optimise scalable pipelines for structured and unstructured data retrieval LLM Training & Fine-Tuning: Use methods like LoRA, QLoRA, SFT, PEFT, and RLHF Inference & Acceleration: Serve models using vLLM, DeepSpeed … 5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face Proven experience with LLMs, RAG, and deploying cloud-native AI on AWS Strong full-stack skills (React, TypeScript, Node.js) and API development Familiarity with vector databases and multi-agent frameworks Apply More ❯

Engineer with full-stack development experience to work on cutting-edge projects involving Generative AI , Retrieval-Augmented Generation (RAG) , and multi-agent reasoning frameworks . This is a hands-on, end-to-end engineering role with impact across the full ML lifecycle – from experimentation … to deployment. Conversational AI & Reasoning: Design, fine-tune, and deploy advanced LLMs with agentic capabilities RAG Pipelines: Build and optimise scalable pipelines for structured and unstructured data retrieval LLM Training & Fine-Tuning: Use methods like LoRA, QLoRA, SFT, PEFT, and RLHF Inference & Acceleration: Serve models using vLLM, DeepSpeed … 5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face Proven experience with LLMs, RAG, and deploying cloud-native AI on AWS Strong full-stack skills (React, TypeScript, Node.js) and API development Familiarity with vector databases and multi-agent frameworks Apply More ❯