1 to 25 of 37 Retrieval-Augmented Generation Jobs in the North West

experience developing applications using Large Language Models (LLMs), including prompt engineering, structured outputs, tool calling, retrieval-augmented generation (RAG) and evaluation techniques. Experience with modern LLM and agentic AI frameworks such as LangChain, LangGraph and Microsoft Foundry or equivalent technologies. Strong understanding of software ...

Engineer you should have: Good understanding of Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), and modern AI frameworks such as LangChain, LangGraph, MCP, with experience developing AI-powered applications. Experience working with vector databases, semantic search, and retrieval optimisation ...

Design and build AI-enabled platforms and intelligent workflows using large language models (LLMs), retrieval-augmented generation (RAG) pipelines and embedding models. Develop scalable backend services and AI integrations, working closely with engineering, architecture and business teams, and support deployment, governance and operational rollout … infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication ...

stakeholders. Key Technical and Soft Competencies Good understanding of Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), and modern AI frameworks such as LangChain, LangGraph, MCP. Experience developing AI-powered applications, working with vector databases, semantic search, retrieval optimisation techniques ...

Senior AI Engineer to design, build and scale Agentic AI, Generative AI and Retrieval-Augmented Generation (RAG) solutions that power intelligent, customer-facing products used globally. Working closely with Product, Platform and Engineering teams, you'll develop production-grade AI systems, autonomous agents … applications, helping shape the future of AI across the organisation. What You'll Do Design and deliver production-ready Agentic AI, Generative AI and RAG solutions. Build autonomous AI agents capable of reasoning, planning and executing complex workflows. Develop multi-agent orchestration, tool integration and advanced retrieval strategies. ...

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 ...

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 ...

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 ...

products and wider AI strategy.You'll design, build and operate the cloud-native platform supporting Retrieval-Augmented Generation (RAG), LLM orchestration, vector search and production AI services, working closely with AI Engineering, DevOps and Security teams to deliver scalable, secure and reliable AI solutions. … Role scope: Designing, building and operating AI platform infrastructure across Microsoft Azure. Developing and optimising RAG pipelines, AI services and platform components. Deploying infrastructure using Terraform and supporting containerised applications. Building and maintaining CI/CD pipelines using GitHub and Azure DevOps. Improving observability, monitoring and platform resilience. Supporting vector ...

evolve and pragmatic decision-making is essential. Nice to Have Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures. Experience working with containerised applications and modern CI/CD pipelines. AWS certifications, including Solutions Architect or Machine Learning Specialty. ...

evolve and pragmatic decision-making is essential. Nice to Have Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures. Experience working with containerised applications and modern CI/CD pipelines. AWS certifications, including Solutions Architect or Machine Learning Specialty. ...

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 ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

productive teams Experience leading teams in fast‐growing startups or enterprise software companies Knowledge of financial planning, analytics, or business intelligence domains Experience with RAG systems, vector databases, and semantic search Familiarity with model fine‐tuning, evaluation, and continuous improvement workflows Background in distributed systems, microservices, or cloud‐native architectures ...

Strong Python engineering capability Experience deploying AI products into real-world environments Experience with several of: LLM applications Agentic AI frameworks Multi-agent systems RAG architectures Vector databases AI orchestration platforms AI evaluation and observability tools Model optimisation and fine-tuning Mindset We're particularly interested in individuals who: Thrive ...

optimisation, data processing) and cloud platforms (AWS, Azure or GCP). Hands‐on experience with a range of AI concepts such as LLMs, VLMs, RAG, agents, MCP, computer vision, GraphML, recommendation systems or reinforcement learning. Familiarity with ML frameworks (PyTorch, TensorFlow, etc.) and strong proficiency in Python and data structures ...

prototypes, products and production ready solutions Designing and implementing end to end AI solutions that integrate with enterprise systems Working with LLMs, prompt engineering, RAG patterns, embeddings and fine tuning Developing AI agents and agentic workflows using modern frameworks Working with vector databases, APIs and modern data platforms Supporting … collaborative, high performing environment Experience Required: Strong Python and SQL experience Applied AI engineering, ML engineering or software engineering background Experience with LLMs, RAG, embeddings, prompt engineering or fine tuning Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks Experience with vector databases such as Pinecone, Chroma ...

Portfolio Credit Control is seeking a Senior AI Engineer to design, build and scale Agentic AI, Generative AI and RAG solutions powering customer-facing products worldwide. You’ll work with Product, Platform and Engineering to develop production-grade AI systems, autonomous agents and LLM-powered applications. You’ll design ...

business environment Deep expertise in Python and modern AI/ML frameworks and SDKs Strong experience building and deploying production‐grade AI solutions, including RAG pipelines and LLM‐based applications Hands‐on experience with AI deployment, operationalisation, monitoring, and scaling Strong understanding of enterprise infrastructure concepts including VNets, Entra ...

shape strategy and drive delivery within a large, complex organisation Knowledge of modern AI approaches, including areas such as Retrieval Augmented Generation (RAG), prompt engineering, observability and evaluation Experience in cyber security, fraud analytics, behavioural analytics or threat detection would be particularly valuable. This ...