24 of 24 Retrieval-Augmented Generation Jobs in the North West

tool execution. Integrate AI agents with APIs, databases, enterprise systems, and third-party platforms. Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and knowledge repositories. Create secure, scalable, and production-ready AI workflows. Engineer prompts, system instructions, and evaluation frameworks to improve agent … memory, reflection, and tool use. Experience building autonomous AI agents and AI-powered automation solutions. Strong prompt engineering and AI evaluation experience. Experience implementing RAG architectures and semantic search solutions. Software Engineering Strong Python development skills. Experience with JavaScript and/or TypeScript. Experience building and integrating REST APIs. Understanding ...

teams to design, develop, and scale production-ready solutions using the latest AI technologies and cloud-native engineering practices. Key Skills: AI & GenAI: LLMs, RAG, Agents, LangChain, LangGraph, CrewAI, Vercel AI SDK, OpenAI, Anthropic, Mistral, Llama. Full Stack Engineering: Python, TypeScript, React, APIs, Microservices, FastAPI. Cloud & DevOps: AWS/Azure … with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

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 ...

cloud-native technologies Integrating Large Language Models (LLMs) into existing and new applications Designing Retrieval-Augmented Generation (RAG) solutions and intelligent knowledge systems stablishing best practice around AI governance, security and scalability Experience required Strong commercial experience with C# and .NET development Experience designing ...

cloud-native technologies Integrating Large Language Models (LLMs) into existing and new applications Designing Retrieval-Augmented Generation (RAG) solutions and intelligent knowledge systems stablishing best practice around AI governance, security and scalability Experience required Strong commercial experience with C# and .NET development Experience designing ...

Design, build, and deploy LLM-powered applications and GenAI solutions Develop and optimise prompt engineering strategies for performance and accuracy Implement RAG (Retrieval-Augmented Generation) pipelines using structured and unstructured data Fine-tune and customise foundation models and LLMs where required. Integrate GenAI solutions … software engineering, data science, or machine learning Proven experience building and deploying LLM/Generative AI applications in production environments Hands-on experience with RAG architectures, embeddings, and vector search Experience working with LLM APIs and frameworks (e.g., OpenAI, Azure OpenAI, Anthropic, Hugging Face) Strong understanding of prompt engineering techniques ...

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 ...

delivering a modern enterprise data platform. This opportunity offers the chance to work on large-scale data initiatives, modern cloud technologies and next-generation analytics capabilities that will support strategic decision-making across the business. The Opportunity You will join a growing data function responsible for building … environments Desirable Experience Microsoft Fabric Power BI development PySpark REST API integrations Lakehouse architectures AI and Machine Learning platforms Retrieval Augmented Generation (RAG) Vector databases Generative AI solutions Power Platform technologies Data transformation or modernisation programmes What We're Looking For We're looking ...

core data science libraries such as pandas, scikit-learn, LangChain, etc. Experience with LLMs, e.g. foundation model APIs, prompt engineering, retrieval augmented generation Substantial depth and breadth across state-of-the-art AI/ML techniques such as Generative AI, NLP, Deep Learning, etc. ...

prioritise AI opportunities across the organisation Required ExperienceEssential Deep expertise in Python and relevant AI/ML frameworks and SDKs Proven experience building RAG pipelines that operate effectively in production environments Hands-on experience with model packaging, deployment, and production AI workflows Strong understanding of enterprise infrastructure concepts including VNets ...

with agents, tool calling or LLM orchestration. A background in lawtech/legaltech or other regulated domains. Programming in Python. Deeper experience with LLMs, RAG techniques and evaluation. Knowledge of Docker, containerisation and serverless technologies. AWS Certification is a plus. Education and Experience: Bachelor’s degree in Computer Science, Information ...

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 ...

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 ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...

experience with Docker, Linux/Unix CLI, Git, and testing Understanding of modern AI approaches, including: Agentic AI systems RAG (Retrieval Augmented Generation) Graph-based approaches Computer vision, audio, or multimodal models Knowledge of data governance and security best practices Strong communication and problem … solutions in secure environments High-performance computing and cloud-native AI architectures End-to-end MLOps lifecycle and model management Advanced AI techniques including RAG, agentic systems, and multimodal models Secure, high-assurance data environments Modern DevOps, CI/CD, and engineering practices Why Join? Work at the forefront ...

their data needs and unblock them quickly. Bring AI Into Your Own Workflow: Use AI tooling to accelerate your own engineering, from code generation to pipeline documentation to data quality checks. Team & Operations Share What You Know: Champion knowledge sharing and best practice across the team and wider … Experience with Terraform or equivalent infrastructure-as-code tooling. Familiarity with statistical, ML or AI concepts, and ideally with building data layers that feed RAG pipelines, agents, or LLM-based applications. Comfort with vector databases, embeddings or semantic search is a plus. Working knowledge of Google's marketing stack: Google ...

used across the business. AI Software Engineer - Responsibilities Design and deliver practical AI solutions Build and maintain LLM-powered applications, including retrieval-augmented workflows and tools integrations Collaborate with product and engineering teams to embed AI capabilities into software suite Establish best practice and champion … Engineer Experience - delivering production-ready solutions using Python, TypeScript or C# Hands on experience building LLM-based applications and solid understanding of retrieval-augmented generation approaches Experience leading projects or parts of projects from design through delivery Product mindset and collaborative approach - able ...

client teams to design, build and deploy AI driven solutions on the platform Translate complex research and business questions into production ready LLM and RAG workflows Debug, optimise and scale LLM applications in real world, client facing environments Build and integrate data pipelines and services across cloud platforms and modern … Your Skills and Experience Strong commercial engineering experience in AI, machine learning or data focused roles Hands on experience with retrieval augmented generation and production LLM workflows Confidence debugging and improving end to end LLM systems in live environments Experience working with modern data ...

Location Manchester, UK (Hybrid - 3 days per week in office) About ServisBOT ServisBOT is building the next generation of AI-driven enterprise automation for regulated industries — a secure, compliant SaaS platform where AI and humans work together to deliver outcomes our clients can trust. We partner with global … implementation in client environments and ensuring we deliver measurable value. You’ll also build confidence in key AI concepts like prompt engineering, LLMs, and RAG, helping translate customer needs into practical, trustworthy AI-enabled solutions. Key Responsibilities Problem Definition & Discovery Support discovery activities for product initiatives and customer engagements Help ...