1 to 25 of 39 Retrieval-Augmented Generation Jobs in the East Midlands

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 ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

gatherings Salary: Senior: €130–150k base + growth trajectory Principal: €160–200k base + package discussion Tech: Python, JavaScript, Azure, AWS, AI, LLM's, RAG Industry: AI Consultancy The Role We're looking for talented engineers to join our customer-facing AI practice, from Senior Forward Deployed Engineers through … components, and solution standards AI Mastery & Innovation Implement AI solutions leveraging LLMs, foundation models, and retrieval-augmented generation (RAG) systems Design evaluation frameworks addressing bias, hallucination detection, and business impact Explore novel AI applications while integrating traditional ML techniques where appropriate ...

/Vertex AI, including fine-tuning, vector search, and low-latency inference. Build end-to-end LLM applications, leveraging RAG (Retrieval-Augmented Generation), agentic workflows, and prompt engineering. Implement robust evaluation frameworks to monitor LLM quality, hallucinations, token usage, and content safety. Develop … Looking For Essential 5+ years' experience in machine learning engineering or applied AI roles. Recent, demonstrable experience with LLMs, Generative AI, and/or RAG-based systems. Strong Python skills using frameworks such as PyTorch, TensorFlow, Hugging Face, or Google GenAI. Experience with vector databases and retrieval-based ...

Practical experience with AWS (and ideally Terraform, GitLab CI). Experience with agentic systems or LLM-powered automation (e.g. tool-augmented LLMs, RAG workflows, agents for complex workflows). If this sounds like you, reach ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

communication, and tactics Familiarity with key agentic patterns and protocols such as A2A (agent-to-agent communication), MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), memory management, and context management. Ability to design effective interaction patterns between agents, tools, and users. Cross-functional collaboration ...

communication, and tactics Familiarity with key agentic patterns and protocols such as A2A (agent-to-agent communication), MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), memory management, and context management. Ability to design effective interaction patterns between agents, tools, and users. Cross-functional collaboration ...

communication, and tactics Familiarity with key agentic patterns and protocols such as A2A (agent-to-agent communication), MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), memory management, and context management. Ability to design effective interaction patterns between agents, tools, and users. Cross-functional collaboration ...

communication, and tactics Familiarity with key agentic patterns and protocols such as A2A (agent-to-agent communication), MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), memory management, and context management. Ability to design effective interaction patterns between agents, tools, and users. Cross-functional collaboration ...

communication, and tactics Familiarity with key agentic patterns and protocols such as A2A (agent-to-agent communication), MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), memory management, and context management. Ability to design effective interaction patterns between agents, tools, and users. Cross-functional collaboration ...

Important Client) profiles against property attributes to surface hyper-relevant travel recommendations. Intelligent Drafting: Build tools that assist managers in drafting complex itineraries, leveraging RAG to ensure every recommendation is backed by real-time perks and partnership data. System Orchestration: Design reliable, async environments for LLM tasks, ensuring source traceability ...

Important Client) profiles against property attributes to surface hyper-relevant travel recommendations. Intelligent Drafting: Build tools that assist managers in drafting complex itineraries, leveraging RAG to ensure every recommendation is backed by real-time perks and partnership data. System Orchestration: Design reliable, async environments for LLM tasks, ensuring source traceability ...