20 of 20 Contract Retrieval-Augmented Generation Jobs in England

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 ❯

ML algorithms : regression, classification, clustering, and deep learning. Hands-on experience with Large Language Models (LLMs) and techniques such as Retrieval-Augmented Generation (RAG) , agent orchestration , prompt engineering , and tool calling . Familiarity with AI standards like Model Context Protocol (MCP) and Agent2Agent (A2A) . Experience with automated testing and validation of AI outputs. More ❯

Cloud Expertise: Hands-on experience with Large Language Models (LLMs) and understanding of performance vs. cost trade-offs Familiarity with Retrieval-Augmented Generation (RAG) , agent orchestration, prompt engineering, and tool calling Knowledge of AI standards like Model Context Protocol (MCP) and Agent2Agent (A2A) Strong grasp of ML algorithms: regression, classification, clustering, deep learning Awareness 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 ❯

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 ❯

and managing databases in Postgres. Optional Qualifications: Built LLM applications and familiar with the concepts of MCP servers, Agents, and Retrieval-Augmented Generation (RAG) systems. Experience with extract, transform, and load (ETL) data with using Python libraries (e.g. Pandas). Strong understanding of version control systems and CI/CD pipelines to streamline development 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 ❯

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 ❯

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 ❯

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 ❯

across the team to ensure informed decisions are made on the application design and testing approaches. . Contributing to technical decision making alongside colleagues, including Generative AI solutions around RAG and prompt engineering. . Contributing to the required documentation and Agile project maintenance responsibilities. . Helping to design and develop incoming features around sentiment analysis, data ingestion and synthetic audience 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 ❯

distributed systems, and enterprise architecture Experience with Claude Code for agentic coding and AI-powered development Proven track record in financial services or regulatory compliance environments Expert knowledge of RAG architectures, advanced RAG patterns, and vector database optimization Experience with Small Language Models (SLM), Agent-to-Agent (A2A) communication, and Model Context Protocol (MCP) Proven ability to architect and scale … frameworks using LangGraph, LangMem, and custom agent orchestration Lead technical strategy for Azure OpenAI GPT-5 integration and advanced embedding-based retrieval systems Design and implement advanced RAG architectures including hybrid search, query routing, and contextual retrieval Establish multi-agent systems with Agent-to-Agent (A2A) communication protocols and Model Context Protocol (MCP) Architect Small Language More ❯

Team, you'll design and build next-generation automation platforms, blending Python, React, Postgres, and modern DevOps practices with cutting-edge AI/ML techniques such as RAG, vector databases, and multi-agent systems. You'll work across the full software lifecycle: from system design and test automation, to deployment and performance monitoring, collaborating with some of the … in the industry. What You'll Work On Designing and developing automation solutions aligned with Apple's product requirements. Building agentic AI/ML systems (MCP servers/clients, RAG pipelines, vector DBs). Developing both front-end (React, JS) and back-end (Python, APIs, Postgres) features. Driving test automation with Selenium and iOS functional frameworks. Implementing CI/CD … experience with Selenium and iOS functional automation. Strong React/JavaScript UI development background. Back-end engineering with Python + Postgres. AI/ML expertise in LLMs, embeddings, and RAG systems. Experience with ETL pipelines, vector databases, and modern DevOps (Jenkins). Bonus points for: Multi-agent system design. Graph database experience. Familiarity with Charles Proxy, Git, Jenkins. Role Details More ❯

independent travel entrepreneurs worldwide, weve always believed in the power of personal service. Now, were using AI to scale care, creativity, and connection like never before. Our next-generation travel platform is being transformed by intelligent features, with TC Co-Pilot at the heart of it. As AI Product Owner, youll lead the charge in embedding smart systems … e-commerce, or hospitality Conversational interfaces, semantic search, or recommendation engines Ethical AI frameworks, GDPR, and trust-by-design principles Building AI features using tools like LangChain, Pinecone, or RAG architectures More ❯

Role: AI Lead Location: London, UK Hybrid: 3 days a week from office JD: AI Lead to drive the development and deployment of next-generation agentic AI solutions using Azure OpenAI GPT-5, LangGraph frameworks, and intelligent document processing. Lead technical workstreams in building production-ready AI systems for financial automation with hands-on development approach. Required Qualifications … Agent evals, DeepEval) Key Responsibilities Design and develop agentic AI applications using LangGraph and LangMem frameworks Build intelligent document processing pipelines using LlamaParse and Azure Document Intelligence Implement advanced RAG systems with text-embedding-3-large and Azure DB for Postgres Lead hands-on development using Claude Code for rapid agentic workflow creation Establish AI observability and monitoring using Arize More ❯

AI Engineer - Defence RAG Systems ( Security Clearance Essential ) Clearance: Active SC Essential | Sector: Defence Role Overview Defence client requires an SC Cleared AI Engineer to build fully on-premises RAG systems using open-source technologies. You'll develop classified AI capabilities on air-gapped infrastructure with zero external dependencies. Key Responsibilities - Build end-to-end RAG pipelines on isolated defence … if required - Demonstrable experience deploying open-source LLMs (Llama, Mistral, Falcon) on-premises - Expertise with local vector databases (Chroma, FAISS, Weaviate) in offline deployments - Strong vLLM/Text Generation Inference experience for high-throughput model serving - Proven ability to work on air-gapped systems with no external package repositories - Experience with GPU orchestration (NVIDIA A100/H100) and More ❯