101 to 125 of 307 Retrieval-Augmented Generation Jobs in England

Anthropic, and Gemini. Implementing and managing multi-API workflows using tools like LiteLLM to ensure flexibility and resilience. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI, rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using MongoDB Atlas Vector Search. Fine … and deep learning frameworks, particularly PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with MLOps principles and tools, including More ❯

are: Experience delivering Large Language Model projects with customers, including LLM API integration, up-to-speed knowledge of foundation models, SFT (Supervised Fine-Tuning), prompt engineering, RAG (Retrieval-augmented generation) and/or measuring AI accuracy. Two years + experience in solutions architecture or integrating multiple applications/data streams, or ML development within More ❯

automate complex legal workflows and enhance user experiences. Advanced Technology Integration: Collaborate on projects that leverage emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs - to enhance our core product and explore new use cases. Cross-Functional Collaboration: Work closely with cross-functional teams to integrate advanced ML models and NLP solutions More ❯

and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker and More ❯

and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker and More ❯

nationwide rollout, while setting the gold-standard for safety, ethics, and governance in a regulated environment. As Director of AI & Research you will spearhead conversational AI, design next-generation AI-powered care models, and define rigorous guardrails to ensure the safety and confidentiality of all users. This is an opportunity to work on greenfield challenges, hands-on experimentation … the roadmap for conversational AI, generative LLMs, and predictive care models from ideation to production. Lead cutting-edge research - run rapid experiments, RLHF loops, fine-tuning, and retrieval-augmented generation to push the boundaries of clinical dialogue systems. Architect safe, scalable solutions - design reference & MLOps architectures on cloud with robust guard rails in place. More ❯

nationwide rollout, while setting the gold-standard for safety, ethics, and governance in a regulated environment. As Director of AI & Research you will spearhead conversational AI, design next-generation AI-powered care models, and define rigorous guardrails to ensure the safety and confidentiality of all users. This is an opportunity to work on greenfield challenges, hands-on experimentation … the roadmap for conversational AI, generative LLMs, and predictive care models from ideation to production. Lead cutting-edge research - run rapid experiments, RLHF loops, fine-tuning, and retrieval-augmented generation to push the boundaries of clinical dialogue systems. Architect safe, scalable solutions - design reference & MLOps architectures on cloud with robust guard rails in place. More ❯

across the business to identify and deliver high-value AI use cases Evaluate and experiment with emerging open-source models and tools (e.g., LLaMA, Mistral, DeepSeek) Design and maintain RAG pipelines and retrieval systems using unstructured data (e.g., PDFs, images, emails) Develop proof-of-concept projects with the Head of Data Science and scale successful solutions into production … Experience: 5+ years of experience with Python for data science and machine learning Strong background in NLP, machine learning, and hands-on work with open-source LLMs Experience building RAG pipelines and working with vector databases and embeddings If you are interested, please reach out for a private conversation- daniel.wexler@source-technology.com Unfortunately, this role does not offer sponsorship More ❯

with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle (including experimentation, training, testing, monitoring, and deployment). More ❯

and maintaining long-lived systems. Bonus Points for: Familiarity with financial services or private equity environments. Experience with Azure, Postgres, Streamlit, or similar tools. Practical experience with document-based RAG, Q&A, or fact extraction tasks. Thriving in small teams with fast feedback and iteration loops. Please, apply to learn more. More ❯

and maintaining long-lived systems. Bonus Points for: Familiarity with financial services or private equity environments. Experience with Azure, Postgres, Streamlit, or similar tools. Practical experience with document-based RAG, Q&A, or fact extraction tasks. Thriving in small teams with fast feedback and iteration loops. Please, apply to learn more. More ❯

and maintaining long-lived systems. Bonus Points for: Familiarity with financial services or private equity environments. Experience with Azure, Postgres, Streamlit, or similar tools. Practical experience with document-based RAG, Q&A, or fact extraction tasks. Thriving in small teams with fast feedback and iteration loops. Please, apply to learn more. More ❯

projects. You are proficient in programming languages such as Python, Java, TypeScript, and Go, and have experience with REST APIs and databases. You understand generative AI methodologies such as RAG, fine-tuning, agents, reasoning workflows, and more. You plan architecture and software with foresight to ensure automation, stability, and maintainability. Knowledge of cloud technologies (Azure or AWS), data science, Kubernetes More ❯

Quantum to share expertise, foster continuous learning, and rapidly re-use best practice ideas. Skills and experience we're looking for: Proficient in latest GenAI practiceslike prompt engineering and RAG systems, with awareness of emerging technologies such as Agentic AI. Skilled in integrating servicesvia Python-based pipelines, including package creation, and developing for user application interfaces. Knowledgeable in Kubernetes infrastructure More ❯

productionize generative AI models. Develop scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning. Define best practices for model monitoring More ❯

productionize generative AI models. Develop scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning. Define best practices for model monitoring More ❯

solutions that transform digital 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 our client's team and … 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 domain-specific models using techniques like LoRA, QLoRA … Milvus, ChromaDB, or OpenSearch. Required 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 and multi-agent orchestration Apply More ❯

solutions that transform digital 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 our client's team and … 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 domain-specific models using techniques like LoRA, QLoRA … Milvus, ChromaDB, or OpenSearch. Required 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 and multi-agent orchestration Apply More ❯

solutions that transform digital 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 our client's team and … 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 domain-specific models using techniques like LoRA, QLoRA … Milvus, ChromaDB, or OpenSearch. Required 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 and multi-agent orchestration Apply More ❯

solutions that transform digital 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 our client's team and … 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 domain-specific models using techniques like LoRA, QLoRA … Milvus, ChromaDB, or OpenSearch. Required 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 and multi-agent orchestration Apply More ❯

solutions effectively. Creating and managing 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 continuous learning and innovation in … solutions under tight deadlines, effectively 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-facing AI platforms (Microsoft Copilot More ❯

that merge the power of Large Language Models (LLMs) with our state-of-the-art computer vision technologies. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI , rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using MongoDB Atlas Vector Search. Fine … deep learning frameworks, particularly PyTorch . Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB . Practical knowledge of LLM fine-tuning , prompt engineering, and performance optimization. A strong interest in computer vision More ❯

Skills & Expertise Experience deploying and managing applications using Azure and Docker. Familiarity with frameworks such as LangChain and expertise in Retrieval-Augmented Generation (RAG) models for AI-driven applications. Proficiency with pandas for data manipulation. Full Stack Developer - What's in it for you? Salary Reviews: Twice a year to recognise your contributions. Generous More ❯

Skills & Expertise Experience deploying and managing applications using Azure and Docker. Familiarity with frameworks such as LangChain and expertise in Retrieval-Augmented Generation (RAG) models for AI-driven applications. Proficiency with pandas for data manipulation. Full Stack Developer - What's in it for you? Salary Reviews: Twice a year to recognise your contributions. Generous More ❯

are: Experience delivering Large Language Model projects with customers, including LLM API integration, up-to-speed knowledge of foundation models, SFT (Supervised Fine-Tuning), prompt engineering, RAG (Retrieval-augmented generation) and/or measuring AI accuracy. Two years + experience in solutions architecture or integrating multiple applications/data streams, or ML development within More ❯