• Defining the model development methodologies and best practices, following the whole life cycle with a central role in choosing the technological stack; • Supporting the productionalization and evolution of the Oracles, entities that through machine learning pilot the behavior of Prima's microservices; • Developing the tools (packages, microservices, pipelines...) that you deem fundamental for your and your team job; • Growing, reading, and experimenting to always be up to speed with emerging technology.

• Defining the model development methodologies and best practices, following the whole life cycle with a central role in choosing the technological stack; • Supporting the productionalization and evolution of the Oracles, entities that through machine learning pilot the behavior of Prima's microservices; • Developing the tools (packages, microservices, pipelines...) that you deem fundamental for your and your team job; • Growing, reading, and experimenting to always be up to speed with emerging technology.

• Defining the model development methodologies and best practices, following the whole life cycle with a central role in choosing the technological stack; • Supporting the productionalization and evolution of the Oracles, entities that through machine learning pilot the behavior of Prima's microservices; • Developing the tools (packages, microservices, pipelines...) that you deem fundamental for your and your team job; • Growing, reading, and experimenting to always be up to speed with emerging technology.

• Model Development: Design, train, and refine machine learning models that tackle real business problems, ensuring they scale effectively in production environments. • Data Pipeline Engineering: Build and maintain robust data ingestion, preprocessing, and transformation pipelines for diverse data sources (structured and unstructured). • AI Workflow Integration: Contribute to end-to-end ML workflows—from serving and monitoring models to evaluating and iterating on their performance. • Advanced AI Techniques: Apply state-of-the-art approaches, including transformers, LLMs, RAG, embeddings, vector databases, predictive modeling, and reinforcement learning, to push the boundaries of what’s possible. • Model Monitoring & Optimization: Support ongoing evaluation and tuning of models to improve accuracy, efficiency, and reliability in production. • MLOps: Help establish best practices for CI/CD, testing, and automated deployment of AI models. • Agile Collaboration: Partner effectively with cross-functional teams in an agile setting, contributing to sprint planning, reviews, and collaborative problem-solving.