1 to 25 of 31 Retrieval-Augmented Generation Jobs in the South East

experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps, and low-code/no-code platforms. Experience across both backend … enable scalable, interoperable systems. Frameworks & Tools: Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms: Skilled in designing and … and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with the Software More ❯

experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps, and low-code/no-code platforms. Experience across both backend … enable scalable, interoperable systems. Frameworks & Tools:Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms:Skilled in designing and … and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with the Software More ❯

experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps, and low-code/no-code platforms. Experience across both backend … enable scalable, interoperable systems. Frameworks & Tools: Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms: Skilled in designing and … and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with the Software More ❯

collaborate with data scientists, ML engineers, and analysts to design and implement solutions that extract insights from unstructured text. Projects may include topic modelling and entity recognition, text generation, ontology creation, and conversational AI. This role demands strong technical expertise in NLP and a passion for innovation and problem-solving. This is a hybrid role based in our … free to ask us about the access to work scheme. Desirable Skills Experience using Azure OpenAI and foundational LLMs for Retrieval-Augmented Generation (RAG), including open-source implementations. Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning PEFT/LoRA, Context embedding, Vector database technologies, and Semantic Search techniques & tools. Familiarity with Azure More ❯

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

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

dependable and consistent results Create and implement intelligent workflow systems, integrating LLMs with enterprise applications, APIs, and automation ecosystems Develop Retrieval-Augmented Generation (RAG) architectures to connect organisational knowledge with LLMs Rapidly prototype and refine AI applications, showcasing tangible business benefits Provide guidance on ethical AI implementation, covering governance frameworks, regulatory compliance, and risk … JavaScript for rapid prototyping and system integration Familiarity with workflow automation solutions (UiPath, Power Automate, Zapier, n8n) and API development Practical knowledge of vector databases and embedding technologies for RAG system implementation Outstanding interpersonal skills - capable of converting business challenges into technical AI strategies Background in agile methodologies or dynamic project environments **Please only apply for this role if you More ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

AI pipelines. What You'll Bring Deep solution architecture experience across cloud, data, and business platforms. Proven experience designing or integrating AI/GenAI solutions such as copilots, chatbots, RAG systems, or automation tools. Understanding of GenAI and agentic design patterns, including orchestration frameworks and RAG. Experience with event-driven systems, messaging platforms, and APIs. Familiarity with AI governance standards More ❯

employee queries, policy navigation). Design and deploy conversational agents using LangChain and AutoGen for internal use. Build modular and scalable pipelines using LangGraph for multi-agent orchestration. Integrate RAG systems to enable context-aware document retrieval from internal HR/legal databases. Required Skills & Experience: Solid exposure in AI/ML engineering, with a strong focus on … NLP and LLMs. Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and RAG Strong proficiency in Python and experience with frameworks like PyTorch or TensorFlow. Experience working with vector databases (e.g., FAISS, Weaviate, Pinecone). Familiarity with HR systems and data (Workday, SAP SuccessFactors, etc.) is a plus. Excellent communication skills and ability to translate technical concepts for non-technical More ❯

for AI engineering across the enterprise, ensuring alignment with organizational priorities and technology roadmaps. Architect for Scale : Drive architectural decisions for platforms and applications that leverage LLMs, retrieval-augmented generation, AI agents, and unstructured data processing at enterprise scale. Influence & Align : Partner with senior engineering leaders, product managers, business stakeholders, and governance teams to … Expertise in modern software architecture, distributed systems, and cloud infrastructure (AWS, Azure, or GCP). Deep hands-on experience integrating AI/ML systems into production environments, including LLMs, RAG, vector search, and AI agents. Strong ability to communicate technical strategy to executives, engineers, and business partners alike. Proven ability to set and enforce engineering standards for quality, security, and More ❯