Hakuun perustuva generointi (RAG) Jobs

I’m a complete beginner who wants to move well beyond theory and into practical, project-based learning. The journey should start with data-science fundamentals—Python, NumPy, pandas, basic statistics, data cleaning and visualization—then progress step-by-step to machine-learning models, large-language-model workflows, retrieval-augmented generation (RAG), agent frameworks, and automation best practices. Hands-on projects are essential; each topic needs to come with a small, self-contained exercise or micro-app that I can build, run, and extend. Think Jupyter notebooks, lightweight Flask or FastAPI endpoints, and scripts that hit real APIs (OpenAI, Hugging Face, LangChain, etc.). Code must be well-commented so I can retrace the logic afterward. Ideal flow • K...

Description We are building a network of specialists who help validate and improve the reliability of enterprise AI systems. Our company, , works with organisations deploying AI assistants and knowledge systems powered by large language models and RAG (Retrieval-Augmented Generation). Your role will be to review AI responses, detect hallucinations, validate grounding against source material, and help create evaluation datasets. This is not model development. This role focuses on AI quality assurance and validation. Responsibilities • Review LLM responses for factual accuracy • Identify hallucinations and fabricated references • Verify whether answers are grounded in provided documents • Detect retrieval vs generation failures in RAG systems • Score responses...