76 to 100 of 279 Retrieval-Augmented Generation Jobs in the UK

practises, including metadata tagging, version control, and retention policies. - Familiarity with AI integration workflows, including semantic indexing, vector databases, and retrieval-augmented generation (RAG). - Knowledge of Microsoft Graph API, Azure AI Foundry, and Copilot integration is a strong plus. - Experience with Filament, LlamaIndex, or similar AI connectors. - Familiarity with enterprise-grade security protocols More ❯

dimensional modelling to deliver consistent, trusted analytics. Enable advanced AI and ML use cases by building pipelines for vector search, retrieval-augmented generation (RAG), feature engineering, and model deployment. Ensure security and governance through robust access controls, including RBAC, SSO, token policies, and pseudonymisation frameworks. Develop resilient data flows for both batch and streaming More ❯

ReAct, Tree-of-Thoughts, and more. Deploy AI/ML pipelines using Azure ML, AWS SageMaker, Vertex AI, or Databricks. Integrate LLMs into production apps using LangChain, LlamaIndex, and RAG architectures. Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with More ❯

LLM, Computer Vision, NLP, Deep Learning Experience with deploying ML models into production An understanding of emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs A proactive mindset to identify problems and create areas for improvement Degree in Computer Science, AI, Big Data, or equivalent More ❯

LLM, Computer Vision, NLP, Deep Learning Experience with deploying ML models into production An understanding of emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs A proactive mindset to identify problems and create areas for improvement Degree in Computer Science, AI, Big Data, or equivalent If interested, and the above applies to More ❯

LLM, Computer Vision, NLP, Deep Learning Experience with deploying ML models into production An understanding of emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs A proactive mindset to identify problems and create areas for improvement Degree in Computer Science, AI, Big Data, or equivalent If interested, and the above applies to More ❯

closely with the team to build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. Contribute to the design and development of full-stack applications for both GenAI and traditional projects More ❯

of web content Prototyping algorithms to optimise ad performance and bidding logic Applying modern LLM techniques — from prompt engineering to retrieval-augmented generation (RAG) Working cross-functionally with engineers, product and commercial teams to bring ideas to life What they’re looking for: 1–3 years’ experience in applied ML/AI (or equivalent More ❯

of web content Prototyping algorithms to optimise ad performance and bidding logic Applying modern LLM techniques — from prompt engineering to retrieval-augmented generation (RAG) Working cross-functionally with engineers, product and commercial teams to bring ideas to life What they’re looking for: 1–3 years’ experience in applied ML/AI (or equivalent More ❯

for managing context and tool interfaces for agents LLM integration patterns including prompt orchestration, multi-agent planning and tool calling Retrieval-Augmented Generation (RAG) for dynamic context injection Model selection/A/B testing/observability What Will Success Look Like Delivers high-quality, maintainable code that meets business needs Takes ownership of More ❯

and mentor a small team of talented engineers. Adjust course based on 6-month worth of data and user feedback (and counting!) Build out our AI infrastructure — things like RAG pipelines , multimodal content handling , and LLM-powered experiences that actually work in production. Experiment with the latest models, tools, and APIs to stay ahead of the curve. Own Data and … are a big plus). Strong background in Python , FastAPI , or similar frameworks. Experience working with LLMs , vector databases , and retrieval-augmented generation (RAG). Hands-on experience deploying AI systems in production. Experience in dealing with IT audits with prospects. A good grasp of cloud infrastructure (Azure, AWS, or GCP). A curiosity More ❯

Design, develop, and refine prompts to maximize the accuracy and effectiveness of GenAI models. Utilize techniques like few-shot prompting, chain-of-thought, and RAG (Retrieval Augmented Generation) to improve model performance within banking applications. Iteratively test and refine prompts based on the evaluation of AI outputs to enhance model performance. Manage the full … Profile Essential Skills/Knowledge/Experience: Strong understanding of Large Language Models (LLMs), including architectures, capabilities, and limits. Familiarity with different GenAI model types, such as text generation, summarization, and question answering. Knowledge of model hallucinations, bias, and context … windows. Mastery of prompting strategies such as zero-shot, few-shot, chain-of-thought (CoT), tree-of-thought (ToT), and self-consistency. Expertise in Retrieval Augmented Generation (RAG) architecture and implementation to integrate external, up-to-date, and proprietary data sources. Knowledge of prompt templating, variable injection, and dynamic prompt generation. Strong SQL skills More ❯

skills Nice to have Understanding and/or hands-on experience of Reinforcement Learning theories, frameworks, and algorithms Experience with cloud infrastructure Experience with Large Language Models (fine tuning, RAG, agents) Experience with graph technology and/or algorithms, understanding of NLP algorithms Our technology stack Python and associated ML/DS libraries (scikit-learn, NumPy, LightGBM, Pandas, TensorFlow, etc More ❯

AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of More ❯

AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of More ❯

experts across 230 locales, to 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 to market 50% faster. Our mission More ❯

like LangChain LangGraph Semantic Kernel Autogen and Crew AI Experience with Hugging Face models and deployment pipelines Knowledge of prompt engineering vector databases and retrieval augmented generation RAG Excellent problem solving communication and collaboration skills Preferred Qualifications Certifications in Azure AI or Microsoft AI Engineer Associate Experience with multiagent orchestration and autonomous systems Familiarity More ❯

Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS/EKS, SageMaker, or Bedrock) Familiarity with LLM frameworks and tooling (e.g. LangChain, vector databases, RAG pipelines) is highly advantageous Genuine interest in AI compliance, governance, and emerging regulation (e.g. EU AI Act, model risk, responsible AI) Strong problem-solving mindset with a passion for building More ❯

Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS/EKS, SageMaker, or Bedrock) Familiarity with LLM frameworks and tooling (e.g. LangChain, vector databases, RAG pipelines) is highly advantageous Genuine interest in AI compliance, governance, and emerging regulation (e.g. EU AI Act, model risk, responsible AI) Strong problem-solving mindset with a passion for building More ❯

Mastery: Strong skills in Python, JavaScript, or similar languages. LLM & Agent Expertise: Hands-on experience with LLMs (OpenAI, Anthropic, Mistral) and agent frameworks. Advanced Tools: Familiarity with vector databases, RAG pipelines, prompt engineering, REST APIs, cloud deployment, and containerization (Docker/Kubernetes). Domain Knowledge: Experience in one or more business domains such as finance, housing, operations, or customer service. More ❯

across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments More ❯

across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments More ❯

best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and More ❯

best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and More ❯

AI Engineers x 2 + fully remote contract + initially 6 months + £500 to £525 per day - Inside IR35 Key Skills: + Design of Gen AI Models + RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use … cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. + Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or … . Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to More ❯