9 of 9 Contract Retrieval-Augmented Generation Jobs in London

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 ❯

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 ❯

sector is looking for a full-stack developer with strong frontend capabilities, ideally in React . The candidate must have hands-on experience with AI/LLM technologies, including RAG, prompt engineering, or GenAI systems, with clear evidence of active contribution to such projects. The role offers flexible working arrangements, with travel to the client's offices in Windsor twice 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 ❯

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 ❯

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 ❯

Design data models for goals, steps, eligibility, location and services, aligning to user needs and product outcomes. Establish data governance Design and document data flows into LLMs (grounding/RAG pipelines, chunking, embeddings, vector indexes, prompt/response schemas, evaluation). Collaborate with Content & Service Designers, AI Engineers, Product & Delivery to prototype and iterate. Embed privacy, safety and ethics Produce … clear artefacts and runbooks for alpha/pilot and handover. Essential experience: Proven Data Architecture for large digital services Hands-on experience feeding data into LLMs/AI apps (RAG, vector stores, grounding, evaluation). Strong data modelling Practical data governance (quality, lineage, catalogues) and excellent stakeholder skills. Knowledge of agentic AI patterns and open interoperability standards (e.g. MCP). More ❯