26 to 50 of 75 Retrieval-Augmented Generation Jobs in the UK excluding London

time management skills. Bonus skills • Experience deploying and managing applications with Azure and Docker. • Familiarity with frameworks like LangChain and Retrieval-Augmented Generation (RAG) models for AI-driven applications. • Experience with pandas for data manipulation. What’s on offer • Regular salary reviews recognising performance and contribution. • Generous annual leave: 25 days plus three days 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 ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

third-party services. Develop front-end interfaces using JavaScript and frameworks such as React. Build and deploy Agentic systems and Retrieval-Augmented Generation (RAG) systems with Large Language Models (LLMs). Participate in privacy automation programs, including workflows for user approvals and reviews. Implement and maintain CI/CD pipelines in Jenkins to streamline More ❯

/Scrum methodologies. Hands-on with Einstein GPT, Prompt Builder/Prompt Studio is desirable. Practical knowledge of prompt engineering, retrieval-augmented generation (RAG) and grounding GenAI on CRM data is desirable. Strong problem-solving and analytical skills. Excellent communication and teamwork abilities are required to present your work internally and give confidence to 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 ❯

huggingface and LLM offerings by 3rd party vendors. Knowledge of OO programming and software design, i.e., SOLID principles. Knowledge and working experience of AGILE methodologies. Familiarity with Databricks, and RAG application architecture a plus Experience with latency optimisation and quantisation preferred Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience and potential. Discretionary bonus More ❯

designing and managing databases in Postgres. AI/ML Expertise: Build and deploy Agentic systems, MCP servers/clients, and Retrieval-Augmented Generation (RAG) systems with Large Language Models (LLMs). Extract, transform, and load (ETL) data to build vector databases with embedding models. DevOps and Maintenance: Implement CI/CD pipelines to streamline More ❯

driven by technological advancements and a visionary approach to future challenges in the AI space. Responsibilities: Build and refine AI/ML models, focusing on LLM-based solutions including RAG, fine-tuning, and prompt engineering. Design and implement AI-powered solutions, leveraging Python, SQL/NoSQL, and APIs for seamless integration. Prototype and rigorously test AI applications, focusing on improving More ❯

on cutting-edge projects, applying machine learning, NLP, and generative AI to real-world business problems. Key Responsibilities Build and refine AI/ML models, including LLM-based solutions (RAG, fine-tuning, prompt engineering). Design and implement AI-powered applications, integrating with APIs, SQL/NoSQL databases, and Python-based solutions. Prototype and test AI applications, optimizing performance and More ❯

on cutting-edge projects, applying machine learning, NLP, and generative AI to real-world business problems. Key Responsibilities Build and refine AI/ML models, including LLM-based solutions (RAG, fine-tuning, prompt engineering). Design and implement AI-powered applications, integrating with APIs, SQL/NoSQL databases, and Python-based solutions. Prototype and test AI applications, optimizing performance and More ❯

ll work with cross-functional teams to define strategy, build innovative solutions, and guide stakeholders on AI adoption. Key Responsibilities Build and refine LLM/SLM-based AI solutions (RAG, fine-tuning, prompt engineering). Design and oversee AI solution architecture (cloud & on-prem). Develop and deploy ML models into production. Identify AI opportunities aligned with business processes Prototype More ❯

ll work with cross-functional teams to define strategy, build innovative solutions, and guide stakeholders on AI adoption. Key Responsibilities Build and refine LLM/SLM-based AI solutions (RAG, fine-tuning, prompt engineering). Design and oversee AI solution architecture (cloud & on-prem). Develop and deploy ML models into production. Identify AI opportunities aligned with business processes Prototype More ❯

security practices including OWASP Top 10 and penetration testing . Familiarity with AI/ML systems , including LLM evaluation techniques, output scoring, and validation frameworks. Understanding of prompt engineering , RAG , model orchestration , and hallucination detection . Awareness of ethical AI , bias detection, explainability, and regulatory compliance. Proven ability to work in Agile teams , solve complex problems, and mentor junior team More ❯

complex reasoning and workflows. Technical Leadership: Serve as the deep technical expert on the platform for both clients and internal teams. Provide guidance on best practices for data integration, RAG implementation, and agentic automation. Proof of Concept & Prototyping: Lead the technical design and development of Proof of Concept (PoC) projects to demonstrate the feasibility and value of our solutions for … event-driven architecture, and MCP-first design principles. AI/ML Acumen: Strong foundational knowledge of modern AI concepts, including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and vector databases (e.g., Qdrant, Pinecone, Weaviate). Data Integration: Hands-on experience designing data pipelines and integrating with diverse enterprise data sources (e.g., relational databases More ❯

Architect multi-step agent workflows using: - Semantic Kernel SDK (C# or Python) - Azure OpenAI (GPT-4, function calling, chat completion) - Planner and Kernel Memory APIs for reasoning and memory - RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in SharePoint, OneDrive, Teams, Outlook, and Planner. utilise More ❯

enterprise-who blends deep technical expertise with sharp delivery instincts. You set the gold standard for safe, scalable AI. Generative AI & Modern Data Platforms Enterprise-grade RAG (Retrieval-Augmented Generation)-ideally on Azure. Agentic AI and M365 Copilot extensibility. Gen AI model strategy, adaptation, and LLMOps. AI safety, governance, and compliance-especially on Azure. More ❯

SINDy) Working knowledge of cell biology. Experience with Python, C, R or related scientific computing languages. Preferred Qualifications - Experience working with causal representation learning Experience with RAG (retrieval-augmented generation) and GraphRAG a big plus Experience with building and deploying software on GitHub, PyPI, Anaconda Cloud, and Docker Hub, as well as use of More ❯

paced environment. Hands-on with Next.js, Typescript (React/Node), Tailwind, SQL (Prisma), and Azure . Bonus points for experience with: LLM APIs (OpenAI, Anthropic), Retrieval Augmented Generation pipelines, Python, serverless architecture, or Microsoft Office Add-ins. Strong product mindset and passion for building. You may have tried a startup yourself or hacked together More ❯

activities Oversee quality assurance, code reviews, and unit testing to ensure delivery quality Ensure AI solutions adhere to secure coding, accessibility, and compliance standards Contribute to implementation of LLMs, RAG, Agentic AI, and related frameworks Manage DevOps pipelines and CI/CD practices in Azure Support delivery of cloud-first applications across Azure, Power Platform, and M365 Research and apply More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯