26 to 50 of 71 Retrieval-Augmented Generation Jobs in the UK excluding London

development! Requirements: Strong Python scripting skills Strong understanding of LLMs Experience delivering Gen-AI projects end-to-end Experience with Retrieval-Augmented Generation (RAG) Experience with Azure AI Foundry would be beneficial Experience with Microsoft data technologies would be beneficial Experience with Cloud platforms - ideally Azure Strong communication, stakeholder management and problem-solving skills More ❯

to £90,000 (DOE) + performance bonus Permanent Hybrid UK nationals only | Must hold SC clearance About the Role An innovative UK technology company is developing a next-generation AI platform designed for defence and health applications. Were looking for a Software Engineer who thrives on learning quickly, solving complex technical problems, and building systems that make a … driven business. What Youll Work On Design, build, and scale intelligent systems and microservices Develop AI-driven capabilities (e.g. LLMs, retrieval-augmented generation (RAG), and AI agents) Build and maintain APIs, data pipelines, and backend components (primarily Python, FastAPI, or Flask) Engineer robust, containerised architectures using Docker and Kubernetes Work with ElasticSearch, Weaviate, Pinecone More ❯

Technology serves nearly 6,000 private and public sector clients across 70 countries. The companys technology independence, global talent, and extensive partner network combine to deliver powerful next-generation IT services and solutions. DXC Technology is recognized among the best corporate citizens globally. The role Were looking for a talented and forward-thinking AI Engineerto join our innovative … architectures (GPT, LLaMA, Mistral, Claude) Fine-tune large language models for domain-specific applications Implement advanced prompt engineering strategies Leverage Retrieval-Augmented Generation (RAG) for enhanced contextual performance Build intelligent agents using frameworks like LangChain, LlamaIndex, CrewAI, AutoGen Apply reinforcement learning techniques including Q-learning , policy gradients , and RLlib Collaborate with cross-functional teams … data science opportunities. Required Skills & Experience Proven experience with Transformer models and LLMs (GPT, LLaMA, Mistral, Claude) Strong background in fine-tuning and prompt engineering Hands-on experience with RAG pipelines Familiarity with Agent Frameworks (LangChain, LlamaIndex, CrewAI, AutoGen) Solid understanding of reinforcement learning concepts and tools (Q-learning, policy gradients, RLlib) Azure AI Engineer Associate certification (or willingness to More ❯

with enterprise partners. No two weeks will look the same. Fine-tune and privately deploy LLMs - with a focus on Retrieval-Augmented Generation (RAG) pipelines Build and scale computer vision systems - from object detection to image segmentation Apply NLP to real-world business problems - summarisation, entity recognition, information extraction, and more Train and deploy More ❯

up. What you'll be doing You'll design and deploy next-gen AI solutions using Microsoft Azure , M365 , and Retrieval-Augmented Generation (RAG) systems. You'll fine-tune large language models (LLMs) for legal-specific applications, uncover insights from complex data, and turn these into intelligent, data-driven decisions that power real business … Collaboration will be key as you'll work closely with designers, engineers, and product managers to bring innovative AI ideas to life. What you'll need Strong experience with RAG architectures or similar retrieval-based AI systems Hands on knowledge of Azure (Databricks) , Python , and SQL A passion for building intelligent systems that drive tangible results Curiosity and More ❯

Foundry and Microsoft Copilot Studio. Proficiency in Python and familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Experience with Large Language Models, prompt engineering, and RAG implementations. Strong skills in data analytics, API development, and MLOps practices including CI/CD for ML. Excellent technical documentation and communication skills. Desirable knowledge in Docker, Kubernetes, and understanding 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 ❯

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 ❯

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 ❯

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 ❯

AI Engineers x 2 + fully remote contract + initially 6 months + £500 to £530 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 ❯

stakeholders Empower teams to scale AI across the organisation Maintain technology RADAR and manage AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in More ❯

stakeholders Empower teams to scale AI across the organisation Maintain technology RADAR and manage AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in More ❯

leading a small, high-performing team. What You’ll Do Build and maintain data pipelines and cloud infrastructure (AWS/GCP). Develop AI-driven applications including LLM tools, RAG pipelines, and automation solutions. Lead on client communication , project delivery, and stakeholder alignment. Collaborate with Data Science and Analytics teams to improve data access, automation, and governance. Drive innovation through More ❯

Solid understanding of REST APIs, cloud platforms (Azure/AWS/GCP), and agile delivery A passion for clean code, scalability, and technical excellence Nice to Have Experience with RAG systems, multi-agent AI, or Langfuse/LiteLLM Python proficiency for AI/ML integration Familiarity with MLOps and cost optimisation for AI systems Background in B2B SaaS or financial More ❯

and Generative AI R&D. Strong understanding of machine learning, data modelling , and natural language processing (NLP) techniques. Experience with fine-tuning , prompt orchestration , and retrieval methods (RAG, knowledge graphs). Familiarity with frameworks such as LangGraph , Azure AI Foundry Agents , and Semantic Kernel Agents . Good understanding of software engineering best practices , including version control, testing, and More ❯

platforms (Azure, AWS, or GCP) Agile mindset with focus on testing, observability, and secure delivery Excellent communication and cross-functional collaboration skills Nice to have Experience with vector databases , RAG systems , or multi-agent AI Python skills for AI/ML development Familiarity with MLOps , Langfuse , or LiteLLM Background in fintech , SaaS , or payments software Join a forward-thinking fintech More ❯

experience building and deploying machine learning models in a production environment. Strong programming skills and deep expertise in Python. Hands-on experience building with agentic or RAG (Retrieval-Augmented Generation) frameworks like LangChain or LlamaIndex. Familiarity with tools for working with Large Language Models via API or in a local context (e.g. HuggingFace transformers More ❯

with Google Cloud Run and Kubernetes (GKE) . Familiarity with GCP data tools like BigQuery and Google Cloud Storage (GCS) . Experience building complex agentic AI applications (beyond basic RAG or LangChain implementations). How We Evaluate Candidates Our interview process is designed to identify practical engineering competence and collaborative skills. Technical Acumen: The technical assessment is rigorous. We evaluate More ❯

Studio or custom frameworks). Clear judgment around latency, cost, and reliability trade-offs. Track record of mentoring and raising engineering standards. Nice to have: Retrieval pipelines, RAG evaluation, Kubernetes, CI/CD for ML, and enterprise security exposure. More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯