• Design and develop AI-powered systems using both traditional ML and generative AI techniques • Build intelligent applications and agents that can perform tasks autonomously or interactively • Create modular, well-documented APIs and service components to enable model integration and consumption • Develop and manage data pipelines, including data collection, transformation, and quality assurance • Implement observability and evaluation mechanisms to monitor AI system behavior • Collaborate with software engineers, data scientists, and solution architects • Support rapid experimentation as well as robust deployment pipelines • Stay informed on the latest trends in GenAI, AI agents, orchestration protocols, and evaluation frameworks • Support project management tasks

• Design, develop, and implement machine learning models and algorithms • Analyze large datasets and extract meaningful insights • Collaborate with cross-functional teams to integrate ML solutions into existing systems • Optimize ML models for performance and scalability • Stay current with the latest advancements in machine learning and AI • Create and implement big data processing pipelines and architectures • Design and build scalable data infrastructure to support ML applications

• Automatizar mediante MLOps la creación de modelos en la plataforma analítica. • Implementar pipelines CI/CD para integración y entrega continuas de código y modelos. • Configurar el proceso gobernado desde Azure DevOps e integrar con otros servicios.

• Design and implement scalable systems for serving deep learning and reinforcement learning models. • Optimize inference performance of deep learning and reinforcement learning models using techniques such as quantization, pruning, and distillation. • Utilize GPU computing to accelerate model training and inference. • Develop and deploy production workflows for training and serving machine learning models. • Collaborate with data scientists and software engineers to design and implement machine learning systems. • Monitor and improve the performance of machine learning models in production. • Stay up-to-date with the latest research and techniques in deep learning and reinforcement learning.