1 to 25 of 45 Retrieval-Augmented Generation Jobs in 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/ ...

using Azure AI Foundry, Copilot Studio, Databricks Mosaic AI and MLflow. Building, optimising and evaluating Retrieval-Augmented Generation (RAG) solutions using prompt engineering and embedding models. Designing and deploying AI agents using frameworks including LangChain, LangGraph and AutoGen. Delivering production-ready GenAI applications … Delta Lake, Unity Catalog, DLT and MLflow. Deep knowledge of Large Language Models including GPT, Llama, Claude and Mistral. Experience building and deploying GenAI, RAG and Agentic AI solutions in enterprise environments. Strong programming skills in Python, SQL and/or Scala. Experience with Azure cloud technologies including Azure OpenAI ...

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 ...

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 ...

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 ...

across the organisation. Build AI assistants, copilots, intelligent automation tools and knowledge management solutions. Develop Retrieval-Augmented Generation (RAG) solutions using internal business data. Create scalable AI architectures and support the ongoing management of AI platforms and infrastructure. Integrate AI solutions with business-critical … experience building and deploying AI or Machine Learning solutions. Strong Python development skills. Experience working with REST APIs and systems integration. Knowledge of LLMs, RAG architectures, vector databases and modern AI frameworks. Experience with AWS cloud services and infrastructure. Familiarity with Docker and Kubernetes. Experience integrating AI solutions with Microsoft ...

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 ...

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 ...

coverageYour qualificationsEarly-career professional with proven commercial experience in:Troubleshooting Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) applicationsRobust prompt and context engineeringData analysis (Excel, Anaplan or similar tools)Software development or quality assuranceLevel 2 or level 3 technical supportA degree in Mathematics ...

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 ...

helping shape its long-term AI strategy Designing and building AI applications, assistants and workflow automation tools Creating Retrieval Augmented Generation (RAG) solutions using internal business data Working with stakeholders to identify opportunities where AI can reduce complexity and improve efficiency Designing scalable … operations, warranty and aftersales Current technologies include: Python SQL OpenAI & Anthropic APIs N8N Copilot Studio PostgreSQL AWS Docker & Kubernetes Experience with AI agents, LLMs, RAG or agentic AI frameworks would be highly beneficial. About you We're open to different backgrounds. You might already be working as an AI Engineer ...

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 ...

moving environment where technologies and best practices are continually evolving. Desirable Experience with Azure OpenAI. Experience building AI agents or agentic workflows. Knowledge of RAG, embeddings and vector databases/search. Experience with Azure AI Search. Familiarity with prompt engineering and evaluation of LLM applications. Experience with Azure DevOps ...

Desirable Experience with other AI platforms (Bedrock) IaC (Terraform/Bicep) for building AI platforms Experience building AI agents or agentic workflows. Knowledge of RAG, embeddings and vector databases/search. Experience with Azure AI Search. Familiarity with prompt engineering and evaluation of LLM applications. Experience with Azure DevOps ...

management SQL and data integration patterns (batch/stream), data quality controls LLM/agent patterns: prompt/version management, tool/function calling, RAG, safety guardrails Observability and production support (logging/metrics/tracing, incident triage) Opening up a world of opportunity. Being open to different points ...

+ 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 ...

platform. This hands-on leadership role focuses on scalable engineering patterns, accelerating delivery, and mentoring development teams. You will design and oversee LLM, RAG, and Agentic AI integrations across Azure, AWS, and GCP, collaborate with architects to ensure secure, maintainable solutions, and set standards for observability, governance, and #J ...

integrating Generative AI/LLM functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working ...

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 ...

equivalentsPrompt engineering, context design and agentic workflow frameworksEvaluation harnesses, evaluation datasets and automated test suites for AI outputsRetrieval-augmented generation (RAG), vector search, embeddings and retrieval pipelines (desirable)LLM orchestration frameworks (LangChain, Semantic Kernel or equivalents – desirable)Technical and Professional ExpertiseStrong software engineering background … GitHub Copilot, Cursor or equivalents, with a practical view of where they help and where they mislead.Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use or fine-tuning in production settings.Experience with LLM APIs, orchestration frameworks, vector search, embeddings, retrieval pipelines, evaluation datasets ...