76 to 100 of 239 Retrieval-Augmented Generation Jobs in the UK

ll work on high-impact projects that bring together the latest advancements in AI, cloud, and full-stack development. Responsibilities: Build proof-of-concept GenAI applications using tools like RAG and intelligent agents Scale prototypes into production-ready systems Design and develop full-stack applications for GenAI and non-GenAI projects Create the infrastructure and tooling to support robust, scalable More ❯

Experience with distributed computing for large-scale ML workloads (Spark). Experience with pricing models or pricing-related projects is strongly preferred. Additional Experience in GenAI projects implementation (multiple RAG patterns, prompt management, agentic workflows) is preferred. You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business 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 ❯

customers to iterate, experiment, and prototype solutions for their business challenges. This role will require a strong consultative approach coupled with critical thinking and hands-on NLP and GenAI (RAG) expertise. You'll be a key member in this team, with significant customer exposure, taking them on the journey from early adoption through to scale. You'll have a solid … be an anchor of the relationship with our customers. Speaking with empathy and credibility will be key. Skills and Experience required 1+ years of focused LLM/GenAI and RAG project experience, preferably in a leadership role; A data science background. NLP experience is key; Fluent in Python, and able to rapidly and independently prototype and develop code, as well … requests and business processes to propose how GenAI may solve business challenges; Chain of thought reasoning to build up LLM based solutions; Resource Augmented Generation (RAG) including optimisation of chunking and metadata; Follow Responsible AI principles; Rapidly develop customer demonstrations to show the art of the possible with GenAI; Contribute to Ascent's Gen AI methodology 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 ❯

Experience working in cloud environments (AWS, GCP, or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g., MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI projects or research publications What We Offer More ❯

understand business requirements and shape technical approaches Contribute to the delivery of scalable, production-ready solutions alongside data engineers and MLOps teams Apply a range of techniques including LLMs, RAG pipelines, vector databases, prompt engineering, and fine-tuning Participate in client workshops and discovery sessions to gather requirements and present findings Stay up to date with the latest trends in 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 ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

AI specialists while maintaining clear documentation Build and deploy Agentic LLM-based solutions with LangGraph. Familiar with different multi agent system patterns Build and deploy LLM-based solutions using RAG Familiar with different types of databases: Relational, Graph etc Design and optimise APIs using Python and FastAPI to serve AI solutions. Familiar with GCP ecosystem and Cloudrun Build and optimise More ❯

and an understanding of how they facilitate agentic AI development. Strong practical knowledge of prompt engineering techniques and advanced LLM concepts, such as CoT reasoning, prompt chaining, iterative refinement, RAG techniques, MCP, multi-agent architectures, and a clear understanding of when each should be applied. Proven experience with Cloud technologies on GCP or AWS (GCP preferred) including serverless architectures and 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 ❯

applications using modern full-stack technologies. Key Responsibilities Develop and deploy AI-powered features into production systems. Build and optimise automation pipelines for scalable delivery. Implement RAG (Retrieval-Augmented Generation) and reasoning models into customer-facing applications. Collaborate with data scientists and engineers to translate technical concepts into real-world tools. Build full-stack … MERN Stack (MongoDB, Express.js, React.js, Node.js or similar..) Strong proficiency in Python, particularly for AI and ML applications. Hands-on experience with PyTorch, GenAI, and reasoning models. Experience implementing RAG architectures in production environments. Solid understanding of both SQL and NoSQL databases Experience building and consuming APIs; familiarity with CI/CD practices. Nice to Have Exposure to cloud platforms More ❯

driven architectures tailored to finance-specific use cases, including: Intelligent document processing (KYC/AML) Conversational finance assistants (customer service, advisory) Automated risk and compliance workflows Synthetic data generation for testing and modeling Personalized financial insights and reporting Select the right GenAI technologies (open-source, proprietary, cloud, or hybrid) to meet client goals without being tied to a … stakeholders in both business and technology. Experience working in or with regulated financial environments and navigating security and compliance requirements. Preferred Qualifications Experience with retrieval augmented generation (RAG), fine-tuning, model evaluation, or deploying models in production environments. Understanding of financial risk modeling, model validation, or operational risk frameworks. Knowledge of regulatory frameworks such More ❯

driven architectures tailored to finance-specific use cases, including: Intelligent document processing (KYC/AML) Conversational finance assistants (customer service, advisory) Automated risk and compliance workflows Synthetic data generation for testing and modeling Personalized financial insights and reporting Select the right GenAI technologies (open-source, proprietary, cloud, or hybrid) to meet client goals without being tied to a … stakeholders in both business and technology. Experience working in or with regulated financial environments and navigating security and compliance requirements. Preferred Qualifications Experience with retrieval augmented generation (RAG), fine-tuning, model evaluation, or deploying models in production environments. Understanding of financial risk modeling, model validation, or operational risk frameworks. Knowledge of regulatory frameworks such 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. Please click here to find out more about our Key Information Documents. Please note that the documents provided contain generic information. If we are successful in finding you an More ❯

team building scalable GenAI solutions, architect Agentic AI components, and integrate them into enterprise systems.Skills- Strong experience in full-stack and cloud-native engineering (Azure preferred) Expertise in GenAI (RAG, prompt engineering, LLM APIs) Deep knowledge of Agentic AI systems and orchestration Proficiency in Python, SQL, NodeJS (React is a plus) Agile leadership and stakeholder collaboration skills Details- 6 month More ❯

months but likely to extend paying £475 per day In order to be considered for this role you will need to have - LLM applications: LangChain, Pydantic-AI (similar frameworks), RAG systems, MCP Servers Front End: some experience with Typescript, REACT Application Servers, web Servers, database Servers, docker Front End (TS/React) and a Back End (Python/FastAPI) leveraging More ❯