21 of 21 Contract Retrieval-Augmented Generation Jobs in the UK

Engineer GenAI | RAG | AI Agents | Python | SQL Locations: Manchester, Leeds, Edinburgh (preferred) | Birmingham & Bristol considered - hybrid working - 2 days per week in either of the above locations Contract initially until Dec 2026. Day rate: up to £800 inside IR35 via umbrella The Opportunity We're looking for an experienced … business teams to deliver innovative AI solutions. Essential Skills & Experience Commercial experience building Generative AI solutions. Strong hands-on experience with RAG (Retrieval-Augmented Generation). Experience developing AI Agents or Agentic AI applications. Strong Python development skills. Strong SQL skills. Experience integrating applications ...

enterprise-scale environment, building the data pipelines and platforms that underpin AI/LLM and Retrieval-Augmented Generation (RAG) solutions. WHAT YOU'LL BE DOING Designing and building scalable ETL/ELT data pipelines Developing cloud-native data solutions within GCP Engineering data platforms …/Python/SQL/Kafka/AI/Generative AI/GenAI/LLM/Large Language Models/RAG/Retrieval-Augmented Generation/ETL/ELT/Data Pipelines/BigQuery/Vector Databases/Enterprise Search/Data Engineering/ ...

using frontier foundation models (eg Claude, GPT, Gemini family) across use cases including agentic automation, retrieval-augmented generation (RAG), copilots, and multi-modal applications Lead technical solutioning during pre-sales and discovery, translating client business challenges into scalable AI architecture and delivery roadmaps Design …/ML or generative AI Proven, hands-on architecture experience with frontier large language models and multi-modal models - including prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design Strong grounding in LLMOps/MLOps: evaluation frameworks, observability, cost and latency optimisation, guardrails, and safe deployment ...

Knowledge of MLOps, DevSecOps, CI/CD, and Infrastructure as Code Experience with vector databases, Retrieval-Augmented Generation (RAG), and AI agent frameworks Exposure to GDS standards and government digital delivery practices Contract Details Role: Lead AI Engineer Rate: £560 per day IR35: Inside ...

help build the data foundations that power enterprise search, knowledge discovery, AI applications and Retrieval-Augmented Generation (RAG). What you'll be doing Design and build scalable ETL/ELT data pipelines across multiple enterprise data sources Develop data solutions using BigQuery, Google ...

platforms. Hands-on experience with AI orchestration and agent frameworks including LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, and AutoGen. Strong knowledge of Prompt Engineering, RAG, Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Azure AI Search), Model Fine-Tuning, LLMOps, and Secure AI practices. Enterprise Architecture & Security Proficiency in enterprise architecture frameworks ...

prompt engineering combined with traditional software/automation engineering. Hands-on expertise utilizing GitHub Copilot, Microsoft Copilot, and Claude Code for code generation, review, and refactoring. Deep proficiency in PowerShell scripting, enterprise automation, and system administration. Strong database background, specifically with SQL Server. Programming proficiency in at least … with Azure AI, OpenAI APIs, or Anthropic APIs. Solid understanding of CI/CD, DevOps, and enterprise automation platforms. Familiarity with RAG (Retrieval-Augmented Generation) architectures, vector databases, and knowledge systems. Previous experience deploying AI solutions within large-scale or regulated enterprise environments. ...

from early foundations to a production-grade, self-service platform: Amazon Bedrock and the surrounding tooling (model access and routing, guardrails, knowledge bases/RAG patterns, agentic workflows), with the observability, cost controls and security posture required to run it in a regulated environment (AI Experience not essential but highly ...

Migration of analytical/data science workloads (convert and migrate R development/Libraries to Databricks). AI/ML, LLM integration, model lifecycle, RAG/vector retrieval or model-serving experience. Large-scale enterprise platform transformation and regulated-industry experience. Strong cost optimisation and FinOps delivery experience ...

Product management, AI Strategy and engineering Hands-on experience building and scaling AI or machine learning products in production environments. Experience understanding LLMs RAG Architecture and agentic workflows Excellent experience with Python. Excellent experience working with AI platforms (Claude, Open AI and AI orchestration frameworks) Experience within Financial services. ...

Experience with AI evaluation frameworks Ability to code independently Strong problem-solving and communication skills Comfortable working autonomously. Technical Experience AI/LLM Evaluation, RAG Evaluation, Ragas, Agentic AI Evaluation, Evaluation Harnesses, LLM Testing and Benchmarking, Prompt Engineering, Python Development, Model and Agent Integration. Ideal Background AI Evaluation Engineer, Applied ...

escalation point Experience with RBAC, data masking, Photon, cluster optimisation or cost management would be beneficial Exposure to Mosaic AI, Vector Search, model serving, RAG or Lakebase would be advantageous ...

Fargate or similar orchestration technologies * NoSQL and relational databases * Messaging and integration services * Cloud storage solutions - Practical experience applying AI technologies such as LLMs, RAG, AI agents and automation within software solutions. In accordance with the Employment Agencies and Employment Businesses Regulations 2003, this position is advertised based upon ...

Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside … eventually run these systems without long-term reliance on external suppliers. Key Responsibilities Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than ...

experience driving cloud/AI transformation within Insurance, Financial Services, or similarly highly regulated industries. Technical Acumen: A strong architectural understanding of GenAI concepts (RAG, AI Agents, Agentic Workflows) and DevSecOps automation. Delivery Mastery: Expertise in benefits management, project planning, and crisis mitigation for large-scale enterprise deployments. To Apply ...

need of a Python engineer to support a customer with building an AI Chatbot using Python and LlamaIndex. Key Technologies: Python 3.12+ LlamaIndex/RAG Azure AI Foundry/OpenAI Typesense/Vector Search PostgreSQL/Pydantic Plotly Dash Key Duties: Develop and enhance production-grade Python AI appli click ...

product management experience, translating customer needs into actionable roadmaps Experience leading or coordinating technical teams Comfortable discussing modern AI/software technologies including LLMs, RAG and data pipelines Strong commercial, scope, budget and delivery management skills Comfortable operating autonomously in a fast-moving environment Excellent communication skills across technical, scientific ...

PrivateLink for cross-cloud data connectivity. AI Readiness : Practical experience architecting data infrastructure for AI/ML consumption - vector databases, embedding stores, and RAG pipeline integration. Soft Skills: Strong interpersonal skills; ability to translate complex data architecture into clear language for Business Analysts and non-technical stakeholders. Duties Data Modelling … warehouse scheduling strategies to reduce operational data spend. AI Readiness: Architect data stores purpose-built for LLM consumption - including vector databases, embedding pipelines, and RAG-compatible data structures that will serve as the foundation for the clients AI product layer. Data Contracts : Partner with Business Analysts to formally define ...

Integrate agents and applications with existing client platforms using Microsoft Graph, REST APIs and Azure services Design solutions involving knowledge retrieval, RAG, document processing and autonomous workflows Advise clients on AI security, governance, identity and deployment considerations Contribute to technical documentation, development standards and solution architecture Work across … Platform , particularly Power Automate and Power Apps Microsoft 365 Copilot and the wider M365 ecosystem Microsoft Foundry/Azure AI services Azure AI Search, RAG and generative AI solutions Conversational AI, agentic AI and autonomous workflows Microsoft Graph and REST API integrations Dataverse, SharePoint and Teams Entra ID, app registrations ...

need of a Python engineer to support a customer with building an AI Chatbot using Python and LlamaIndex. Key Technologies: Python 3.12+ LlamaIndex/RAG Azure AI Foundry/OpenAI Typesense/Vector Search PostgreSQL/Pydantic Plotly Dash Key Duties: Develop and enhance production-grade Python AI applications … chatbot capabilities. Build and improve RAG pipelines, retrieval and document search. Extend LlamaIndex agents, workflows, retrievers and tool-calling capabilities. Integrate LLMs, APIs, databases and structured organisational data. Improve AI answer quality, citations, performance, security and reliability. Deliver well-tested, documented code and support technical handover. Key Requirement ...

need of a Python engineer to support a customer with building an AI Chatbot using Python and LlamaIndex. Key Technologies: Python 3.12+ LlamaIndex/RAG Azure AI Foundry/OpenAI Typesense/Vector Search PostgreSQL/Pydantic Plotly Dash Key Duties: Develop and enhance production-grade Python AI applications … chatbot capabilities. Build and improve RAG pipelines, retrieval and document search. Extend LlamaIndex agents, workflows, retrievers and tool-calling capabilities. Integrate LLMs, APIs, databases and structured organisational data. Improve AI answer quality, citations, performance, security and reliability. Deliver well-tested, documented code and support technical handover. Key Requirement ...