51 to 75 of 226 Retrieval-Augmented Generation Jobs in England

impact and are excited about working in a high-growth startup. Nice to have: experience with Vector databases, and implementing retrieval-augmented generation (RAG) systems and data pipelines Nice to have: exposure to evaluation systems, including running experiments, labeling, user feedback, and scoring methods. Note: If you feel you dont have the whole experience More ❯

is an exciting opportunity to work in a greenfield environment, where you will play a crucial role in designing and deploying secure, scalable AI services that drive next-generation use cases, including client intelligence, document processing, and risk management. Key Responsibilities: Architect & Implement: Design and deploy secure AI services from lab to … production, ensuring they are scalable and compliant with industry standards. API Development: Create robust APIs for large language models (LLMs), retrieval-augmented generation (RAG) pipelines, agentic workflows, and document intelligence systems. Cybersecurity & Privacy: Integrate cybersecurity and data privacy controls across all AI workflows, including encryption, anonymisation, and access logging. Collaborate with CISO: Work closely … workflows using modular LLM agents with memory, planning, and tool integration. Experience implementing Model Context Protocol (MCP) for secure, auditable context injection across agentic systems. Demonstrated ability to build RAG pipelines with strict data governance and contextual integrity. Familiarity with regulatory frameworks such as the EU AI Act, FCA cybersecurity principles, and oversight of critical systems. Previous collaboration with cybersecurity More ❯

is an exciting opportunity to work in a greenfield environment, where you will play a crucial role in designing and deploying secure, scalable AI services that drive next-generation use cases, including client intelligence, document processing, and risk management. Key Responsibilities: Architect & Implement: Design and deploy secure AI services from lab to … production, ensuring they are scalable and compliant with industry standards. API Development: Create robust APIs for large language models (LLMs), retrieval-augmented generation (RAG) pipelines, agentic workflows, and document intelligence systems. Cybersecurity & Privacy: Integrate cybersecurity and data privacy controls across all AI workflows, including encryption, anonymisation, and access logging. Collaborate with CISO: Work closely … workflows using modular LLM agents with memory, planning, and tool integration. Experience implementing Model Context Protocol (MCP) for secure, auditable context injection across agentic systems. Demonstrated ability to build RAG pipelines with strict data governance and contextual integrity. Familiarity with regulatory frameworks such as the EU AI Act, FCA cybersecurity principles, and oversight of critical systems. Previous collaboration with cybersecurity More ❯

transport industry and/or geographical information systems (GIS) Experience with cloud infrastructure Understanding of NLP algorithms and techniques and/or experience with Large Language Models (fine tuning, RAG, agents) Experience with graph technology and/or algorithms Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph TensorFlow, etc ) PySpark More ❯

you Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline Understanding of NLP algorithms and techniquesand/or experience with Large Language Models (fine tuning, RAG, agents) Are proficient with Python, including open-source data libraries (e.g Pandas, Numpy, Scikit learn etc.) Have experience productionising machine learning models Are an expert in one of predictive modeling More ❯

transport industry and/or geographical information systems (GIS) Experience with cloud infrastructure Understanding of NLP algorithms and techniques and/or experience with Large Language Models (fine tuning, RAG, agents) Experience with graph technology and/or algorithms Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph, , TensorFlow, etc...) PySpark More ❯

you... Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline Understanding of NLP algorithms and techniquesand/or experience with Large Language Models (fine tuning, RAG, agents) Are proficient with Python, including open-source data libraries (e.g Pandas, Numpy, Scikit learn etc.) Have experience productionising machine learning models Are an expert in one of predictive modeling More ❯

into prompt-ready inputs and memory chains Implement prompt chaining, context injection, and result validation patterns Use vector search and retrieval-augmented generation (RAG) to ground AI reasoning in fact Work with engineers and product leaders to align output quality with strategic goals Continuously test and refine prompts through performance benchmarking and feedback loops More ❯

cloud technologies and services (e.g., AWS, Azure, Google Cloud) Experience with SQL and/or Snowflake. Some experience with Generative AI technologies (e.g. Bedrock, Langchain, LangGraph). Terms like RAG and Graphs are nothing new for you. Proficiency in data preprocessing and feature and/or prompt engineering. Customer-focused engineer with a passion for crafting high-quality digital products More ❯

cloud technologies and services (e.g., AWS, Azure, Google Cloud) Experience with SQL and/or Snowflake. Some experience with Generative AI technologies (e.g. Bedrock, Langchain, LangGraph). Terms like RAG and Graphs are nothing new for you. Proficiency in data preprocessing and feature and/or prompt engineering. Customer-focused engineer with a passion for crafting high-quality digital products More ❯

features in complex business contexts Strong understanding of machine learning algorithms, NLP, and LLMs with demonstrated business application expertise Experience developing AI-powered automation systems, intelligent assistants/copilots, RAG systems, voice interfaces, and computer vision applications (image and video processing) for enterprise environments Knowledge of advanced AI agent frameworks and architectures such as ReAct for building more effective autonomous More ❯

LLM & Generative AI Engineer. Description Work on emerging GenAI projects Design and Develop state of the art language models Develop proof of concepts Explore Retrieval Augmented Generation (RAG) methods Solve industry relevant problems Design and implement artificial intelligence systems and applications that can simulate human intelligence processes through the creation and validation of algorithms … Software Engineering background in C++, Python or similar NLP Tasks (Natural Language Processing) Experience with LLMs (Large Language Models) Hands on experience with RAG techniques (Retrieval Augmented Generation) Generative AI Job Offer Private Medical Bonus (18%) Private Pension 25 days + BHs Flexible working More ❯

and experience implementing CI/CD pipelines. Hands-on experience with Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (Traditional RAG), and Agentic RAG. Proficiency in front-end development using HTML, CSS, JavaScript, and React. Experienced with Git for version control, branching strategies, and collaborative development practices. Excellent verbal and written More ❯

with modern AI tooling and ecosystems, including Hugging Face, Cursor, vector DBs, and productivity tools that accelerate GenAI development Expertise in GenAI and LLMs, with hands-on experience in RAG solutions and agentic frameworks; capable of leading end-to-end design and deployment of GenAI-driven systems Proven ability to manage projects with expert team members and to provide inspiring More ❯

Bedrock, DataRobot, and NVIDIA AI Enterprise. Experience designing, building, and operating gen-ai environments. Proven ability to design and white board end to end Retrieval Augmented Generation (RAG) pipelines-including data ingest, vector DB selection, orchestration flow, and storage and to explain trade offs to customer architects. You have experience selling new technologies with More ❯

current project. Provide guidance to more junior members of the team, while orchestrating the work of the entire team. Good to have experience applying GenAI techniques like prompt engineering, RAG, or LLM fine-tuning to solve business problems. What You'll Bring 5-7 years of consulting experience or relevant industry experience, with at least 1+ years at a project More ❯

on large language models (LLMs) and generative AI. Hands-on expertise with Azure Databricks , including data engineering, model development, and orchestration of ML workflows. Practical experience using LLMs and RAG , including prompt design, vector databases, model deployment, and integration into applications. Strong Python development skills , with experience building scalable, maintainable AI services and APIs. Experience integrating AI models into real More ❯

/ML systems and MLOps pipelines with hands-on experience with state-of-the-art data and ML frameworks (PyTorch, HuggingFace, Spark, Langchain), technical familiarity with LLMs/FMs, RAG, and prompt engineering techniques. PREFERRED QUALIFICATIONS - History of successful technical consulting and/or architecture engagements with large-scale customers or enterprises. - Experience migrating or transforming legacy customer solutions to 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 ❯

to deployment and maintenance Familiarity with implementing solutions leveraging Large Language Models, as well as a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architecture Experience with graph machine learning (i.e. graph neural networks, graph data science) and practical applications thereof This is complimented by your experience working with Knowledge More ❯

communication of complex ideas. Ability to work independently and collaboratively. Preferred Skills: Experience building scalable applications with LLMs using frameworks like LangChain, LlamaIndex, Hugging Face, etc. Deep knowledge of RAG implementation and enhancements. Benefits & perks (UK full-time employees): Generous PTO and holidays. Comprehensive medical and dental insurance. Paid parental leave (12 weeks). Fertility and family planning support. Early More ❯

secure cloud development practices and IAM role design. Understanding of LLM fine-tuning, embeddings, vector stores (e.g., Pinecone, FAISS, OpenSearch). Exposure to contact centre automation, conversational agents, or RAG pipelines. Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities More ❯

Proficient in problem-solving and analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX. Experience with Large Language Models, including RAG and fine-tuning techniques. Familiarity with compute infrastructure necessary to support operating AI and ML technology. For more information about DRW's processing activities and our use of job applicants More ❯

Strong understanding of AI technologies, including machine learning, natural language processing, or computer vision; or mathematical optimization. Experience with LLMs and Generative AI techniques such as fine tuning, pruning, RAG systems, transfer learning etc. Excellent command of Python, Docker and Git. Experience with GitHub/Gitlab workflows and functionalities. Solid understanding of agile development methodologies and best practices. Excellent leadership More ❯

driven ML architectures (e.g. Kafka, Flink, Spark Structured Streaming) Experience working in regulated domains such as insurance, finance, or healthcare Exposure to large language models (LLMs), vector databases, or RAG pipelines Experience building or managing internal ML platforms, experimentation frameworks, or feature stores Theres something for everyone. Were a place of opportunity. Youll have the tools and autonomy to drive More ❯