Overview We are looking for Generative AI Specialists (junior, intermediate, and middle-senior profiles) to join BIP's xTech Competence Centre, one of the largest Data Science communities in Europe. You will work on advanced AI projects for leading enterprise clients across multiple industries, developing Generative AI solutions for real business use cases. You will be involved in initiatives leveraging pre-trained models (such as LLMs) as well as training and fine-tuning new models, especially in contexts with unstructured data (text, images, video, and audio).
Master's Degree in Computer Engineering, Mathematical Engineering, Computer Science, Statistics, or related STEM disciplines;
Passion and expertise in data exploration, data analysis, and model development;
Strong knowledge of Statistics, Data Mining, Machine Learning, and Deep Learning;
Solid programming experience in Python;
Experience with version control systems (e.Strong knowledge of relational databases and SQL programming;
Knowledge of NoSQL databases (e.Elasticsearch, MongoDB, Redis);




Experience with vector databases (e.Experience using AI Cloud APIs from at least one major provider (Google Cloud, Microsoft Azure, AWS);
Experience processing unstructured data;
Experience with Neural Networks, CNNs, and Transformers;
Experience inneural network training and fine-tuning techniques (e.Experience with major deep learning libraries (TensorFlow, PyTorch) and model repositories (Hugging Face);
Fluent professional proficiency in Spanish and English (mandatory).
Experience with containerization tools (Docker, Kubernetes);
Experience developing and applying Generative AI models in industrial environments;
Software development experience and strong knowledge of SDLC;
Experience with data/model versioning frameworks (MLflow, DVC);
Experience with generative networks for controlled content production (text, images, video);
Experience working in Agile frameworks (Scrum, Kanban);
Knowledge of Data Engineering conce

📌 Data Scientist (Madrid)
🏢 Bip Spain
📍 Madrid