21 of 21 Permanent Retrieval-Augmented Generation Jobs in West Yorkshire

ENGINEER - AI AGENTS/RAG/PYTHON/SQL/APIs £750-£800 per day | Inside IR35 | 3-month initial contract | Manchester or Leeds | Hybrid THE DETAILS £750-£800 per day Inside IR35 Initial 3 months with likely extensions Manchester or Leeds 2 days per week on-site … Agents/Agentic AI/Artificial Intelligence/Generative AI/GenAI/LLM/Large Language Models/RAG/Retrieval-Augmented Generation/Prompt Engineering/Prompt Writing/APIs/API Testing/API Integration/Python/SQL/Enterprise ...

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/ ...

understanding of generative AI and the modern AI stack, including large language models, prompt engineering, retrieval-augmented generation (RAG), vector databases, evaluation/monitoring and MLOps principles, with the ability to shape investment cases and operating models at scale.Extensive experience providing executive oversight ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Apply clean code, automated testing, CI/CD, observability and other modern engineering principles Build and integrate AI-enabled features, including LLM-powered services, RAG and agent-based workflows where appropriate Use AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor or similar to improve engineering productivity Develop … Desirable: Experience integrating AI into production systems (e.g. chat interfaces, automation, intelligent search) Familiarity with Retrieval-Augmented Generation (RAG), vector databases, or embeddings Experience with cloud platforms (AWS preferred) Awareness of emerging AI tooling (e.g. MCP, A2A, Threads or similar) Open-source contributions ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

+ security considerations). Be the AI champion/evangelist within the tech team. You will set reference architectures for predictive ML, generative AI, RAG, and agentic systems; choose platforms and patterns teams can reuse; and hold the line on quality, governance, and operational readiness. The role requires strategic thinking ...

+ security considerations). Be the AI champion/evangelist within the tech team. You will set reference architectures for predictive ML, generative AI, RAG, and agentic systems; choose platforms and patterns teams can reuse; and hold the line on quality, governance, and operational readiness. The role requires strategic thinking ...

detection that balance performance, explainability, and operational risk. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence ...

capabilities, and appropriate use cases. Strong understanding of AI, ML and LLM concepts, including prompt engineering, prompt grounding, iterative loop techniques, context windows, embeddings, RAG, agentic workflows. Proven ability to integrate AI capabilities both into low-code automation flows and high-code stacks, including, applications, APIs, microservices, distributed systems … code and high‐code platforms. Hands‐on use of AI coding assistants (e.g., GitHub Copilot, Claude Code) and autonomous software engineering agents. Exposure to RAG, vector databases, embeddings, and AI retrieval systems. Experience working with cloud AI services and orchestrating AI agents. Exposure to DevOps practices, CI/ ...

transformation programme. You will design and build scalable data pipelines in GCP, develop cloud-native solutions, and support AI/LLM and RAG initiatives across a large-scale environment. You will work on event-driven architectures with Kafka, write robust Python and SQL, and help ensure data quality, governance ...

company's internal AI platform and turn successful approaches into reusable tools and accelerators. What you'll be working on: Building AI solutions using RAG, AI agents, LLM integrations and MCP Taking solutions from prototype through to production, including security, scalability, guardrails and observability Building AI evaluation and testing frameworks … languages Full-stack capability, including back-end and data engineering Proven experience building and shipping production LLM/AI applications Hands-on experience with RAG, AI agents, prompt engineering, tool orchestration and MCP Experience with AI evaluation, testing, datasets and guardrails Client-facing experience and an understanding of enterprise security ...

enable more reliable and scalable product development Establish end-to-end technical architectures for LLM-enabled systems, covering data pipelines, retrieval/RAG, prompt orchestration and agent workflows to support robust and effective AI implementation Analyse complex constraints across product, data, security, legal and operations, translating them into … services and the supporting platform services required for production delivery AI engineering experience delivering LLM-enabled systems using LLM APIs/Bedrock, including RAG architectures, vector databases and orchestration frameworks DevOps skills including CI/CD pipelines, containerisation and monitoring/observability, with experience defining release and operating practices ...

enable more reliable and scalable product development Establish end-to-end technical architectures for LLM-enabled systems, covering data pipelines, retrieval/RAG, prompt orchestration and agent workflows to support robust and effective AI implementationAnalyse complex constraints across product, data, security, legal and operations, translating them into clear … services and the supporting platform services required for production delivery AI engineering experience delivering LLM-enabled systems using LLM APIs/Bedrock, including RAG architectures, vector databases and orchestration frameworks DevOps skills including CI/CD pipelines, containerisation and monitoring/observability, with experience defining release and operating practices ...

Loop and MLOps.Data Architecture Design: You understand and know when to use the following data architecture design patterns RAG (Retrieval-Augmented-Generation), Serverless, DWH models, Data Mesh and Data Products.Hands on mindset: You speak the language of data fluently, whether it's Python ...

workflows in Copilot Studio Build scalable components: prompt orchestration, retrieval layers, Power Automate flows, model interfaces, validation pipelines Develop and optimise RAG components - embeddings, vector queries, metadata strategies for accuracy and reliability Integrate AI agents with enterprise systems via Microsoft Graph, APIs, and Power Platform connectors Implement secure … support the satellite model across business functions Requirements Hands-on experience building solutions with LLMs, AIAPIs, Copilot Studio, or agent frameworks Strong understanding of RAG architectures, vector databases, embeddings, and retrieval optimisation Experience with Microsoft Azure AI services, cloud-native engineering, and secure deployment patterns Experience with agent ...

Searchability seeks an experienced AI Engineer to join a major enterprise AI transformation programme, focusing on AI Agents, RAG and GenAI. You’ll design and build agentic AI solutions, and develop APIs in a complex enterprise environment. The role involves working with large language models, prompt engineering, and production-grade ...

will build scalable components, integrate with enterprise systems, and implement secure AI practices within a modern cloud-native stack. The role focuses on RAG architectures, vector databases, embeddings, and retrieval optimization, with emphasis on observability, governance, and reusable code assets. Collaborative, cross-functional work is expected. #J ...

strategy, roadmap and architecture. Design secure, scalable and reusable AI solutions and implementation patterns. Identify opportunities to apply Generative AI, LLMs, AI agents, RAG and automation. Develop new AI-enabled products and services, taking solutions from pilot through to production. Create reusable architectures, frameworks, documentation and implementation guidance. Drive internal … responsible AI practices. Key Skills & Experience The successful candidate will have strong experience across: AI & Generative AI: LLMs, foundation models, AI assistants, agents, RAG, enterprise search and workflow automation. Solution & Platform Architecture: Designing scalable, resilient and supportable enterprise solutions. Data & Integration: Data architecture, governance, structured/unstructured data, APIs, search ...

strategy, roadmap and architecture. Design secure, scalable and reusable AI solutions and implementation patterns. Identify opportunities to apply Generative AI, LLMs, AI agents, RAG and automation. Develop new AI-enabled products and services, taking solutions from pilot through to production. Create reusable architectures, frameworks, documentation and implementation guidance. Drive internal … responsible AI practices. Key Skills & Experience The successful candidate will have strong experience across: AI & Generative AI: LLMs, foundation models, AI assistants, agents, RAG, enterprise search and workflow automation. Solution & Platform Architecture: Designing scalable, resilient and supportable enterprise solutions. Data & Integration: Data architecture, governance, structured/unstructured data, APIs, search ...