10 of 10 Retrieval-Augmented Generation Jobs in the North West

Role) This hands-on role requires a strategic builder responsible for: - GENAI LEADERSHIP: Designing, developing, and fine-tuning cutting-edge Generative AI applications (e.g., chat systems, knowledge assistants). - RAG ARCHITECTURE: Mastering and deploying robust Retrieval-Augmented Generation (RAG) pipelines, integrating LLMs with complex enterprise data sources for unparalleled accuracy. - END-TO-END DELIVERY … ML techniques. CANDIDATE PROFILE: The ideal candidate will possess 4–5 years of dedicated professional experience and demonstrable proficiency across: - CORE EXPERIENCE : Strong command of Generative AI, LLMs, and RAG principles. Proven experience managing the end-to-end ML lifecycle. - TECH STACK: Proficiency in Python (Pandas, NumPy, LangChain). Deep familiarity with the [Specific Vendor]’s [Cloud Environment] AI and More ❯

development! Requirements: Strong Python scripting skills Strong understanding of LLMs Experience delivering Gen-AI projects end-to-end Experience with Retrieval-Augmented Generation (RAG) Experience with Azure AI Foundry would be beneficial Experience with Microsoft data technologies would be beneficial Experience with Cloud platforms - ideally Azure Strong communication, stakeholder management and problem-solving skills More ❯

development! Requirements: Strong Python scripting skills Strong understanding of LLMs Experience delivering Gen-AI projects end-to-end Experience with Retrieval-Augmented Generation (RAG) Experience with Azure AI Foundry would be beneficial Experience with Microsoft data technologies would be beneficial Experience with Cloud platforms - ideally Azure Strong communication, stakeholder management and problem-solving skills More ❯

development! Requirements: Strong Python scripting skills Strong understanding of LLMs Experience delivering Gen-AI projects end-to-end Experience with Retrieval-Augmented Generation (RAG) Experience with Azure AI Foundry would be beneficial Experience with Microsoft data technologies would be beneficial Experience with Cloud platforms - ideally Azure Strong communication, stakeholder management and problem-solving skills More ❯

stakeholders Empower teams to scale AI across the organisation Maintain technology RADAR and manage AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in More ❯

stakeholders Empower teams to scale AI across the organisation Maintain technology RADAR and manage AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

and recommend relevant advancements Requirements : 2-5+ years experience in an AI-focused enterprise/start-up Strong Python skills Strong practical experience in LLM concepts (context engineering, RAG, tool calling, workflows, multi-agent systems, MCP) Hands-on experience with frameworks like LangChain, Llamaindex, etc., vector stores, LLM APIs (e.g. OpenAI API, Anthropic API), and building MCP servers/ More ❯

environment. What You’ll Do Train and fine-tune LLMs and ML models using standardised experiments and high-quality data. Build and improve LLM-based systems for low-latency RAG systems, intent detection, call scoring, and email classification. Support real-time inference infrastructure with ultra-low latency and detailed monitoring. Create and maintain evaluation datasets to test safety, prompt following … and drive to improve model performance. Why Join Us Work on real applied LLMs from training to deployment. Collaborate with an ambitious, research-informed team. Shape the next generation of AI-driven communication tools. More ❯