1 to 25 of 42 Retrieval-Augmented Generation Jobs in the North West

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 ...

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 ...

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/ ...

Experience designing multi-agent systems or autonomous workflows. Hands-on experience with Docker and deploying containerised, cloud-native tools. Experience with advanced retrieval-augmented generation techniques, including: TAG (Tool-Augmented Generation) – integrating external tools to enhance generation capabilities. … Context-Aware Generation) – leveraging dynamic context to improve relevance and coherence. GraphRAG (Graph-Augmented Retrieval-Augmented Generation) – utilizing graph-based structures to enrich retrieval and reasoning. This will include the competencies Stakeholder engagement: Works with cross-functional teams ...

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 ...

environment. Define technical standards and best practices for developing advanced Generative AI applications, encompassing complex Retrieval-Augmented Generation (RAG) architectures, prompt and context engineering, and Large Language Model (LLM) integration strategies. Architect innovative Agentic AI systems, establishing frameworks for orchestration, memory management, and robust … Azure environments, with a focus on technical escalation and resolution. Expertise in establishing technical standards and best practices for advanced Generative AI applications, encompassing RAG architectures, prompt engineering, and LLM integration strategies. Experience in architecting and developing Agentic AI systems, including frameworks for orchestration, memory management, and performance evaluation. Advanced ...

existing business systems and platforms using REST APIs and cloud-native architectures. Build and optimise Retrieval-Augmented Generation (RAG) solutions, embeddings, and vector database implementations. Develop AI Agents and Multi-Agent Systems to automate complex business processes. Apply prompt engineering and context engineering techniques … business stakeholders to drive AI adoption and innovation. Required Experience: Strong background in Generative AI, LLMs, NLP, and AI Engineering. Hands-on experience with RAG, embeddings, and vector databases. Proven experience developing AI Agents and agentic workflows. Proficient in Python, SQL, and API integration. Familiarity with cloud platforms such ...

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 FullStack applications for both GenAI and non-GenAI projects. ...

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 ...

experience of this would be really useful. Building agentic AI solutions, LLM-powered applications and retrieval-augmented generation (RAG) architectures. Experience with Vertex AI Agent Builder, Agentspace, LangGraph or multi-agent frameworks. Knowledge of responsible AI, model governance and risk management practices within regulated ...

Build and integrate AI-powered features into web applications and enterprise tools. Build and optimize Retrieval-Augmented Generation (RAG) solutions to power chatbot interactions Work both independently and paired with other team members to ensure efficient delivery of prioritised features. Take part … Experience using modern AI-assisted and agentic development workflows in day-to-day engineering usingAI/ML APIs and SDKs Hands-on experience developing RAG solutions or working with LLMs for chatbots is preferred In-depth understanding of CI/CD and the tools used to implement them, such ...

both customers and organisations. Modern AI architecture expertise. Deep understanding of machine learning, LLMs, retrieval-augmented generation (RAG), agentic systems and the architectural patterns used to deploy AI solutions at enterprise scale. Engineering and cloud foundations. Strong understanding of software engineering, data architecture ...

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 ...

most cutting-edge defence and aerospace projects in the UK? If the prospect of developing software for projects such as a sixth-generation stealth fighter jet or a revolutionary Laser Directed Energy product excites you, then Leonardo would like to hear from you. Leonardo is seeking to recruit … fine-tuning large language models (LLMs). Experience building scalable pipelines, including APIs, vector databases, retrieval-augmented generation (RAG), and orchestration frameworks. Experience curating datasets, managing embeddings, and ensuring security, privacy, and responsible AI practices. Experience in prompt engineering, with the ability to design ...

production-ready solutions. Key Responsibilities Design and develop AI-powered applications using GenAI , LLMs , NLP , and Agentic AI technologies. Build intelligent RAG (Retrieval-Augmented Generation) solutions leveraging Embeddings and Vector Databases . Develop and orchestrate AI Agents and Multi-Agent Systems to automate complex … Language Models (LLMs) , Natural Language Processing (NLP) Prompt Engineering , Context Engineering AI Agents , Agentic AI , Multi-Agent Systems LangChain , LangGraph , Model Context Protocol (MCP) RAG , Embeddings , Vector Databases Python , SQL , REST APIs Git , CI/CD Cloud Platforms (Azure, AWS, or GCP) LLM & AI Agent Testing and Evaluation Desirable Skills ...

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 ...

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 ...

product estate. Disciplined use of AI tools across the delivery process with the ability to validate output, supported by optional experience in production LLMs, RAG, and AI agents. Proven background in customer-facing technical delivery, spanning pre-sales technical discovery, hands-on implementation, and escalation management. Excellent communication skills ...