• Study and transform data science prototypes using appropriate ML and GenAI architectures. • Design and develop machine learning and LLM-powered systems using modern GenAI architectures (e.g., RAG, prompt engineering, embeddings, vector databases). • Solve complex problems with multilayered data sets and optimize existing machine learning libraries and frameworks. • Construct optimized data pipelines to feed both traditional ML models and LLM-based systems. • Run machine learning tests and experiments and document findings and results. • Implement and monitor model and data quality checks to ensure accuracy and consistency of our models and pipelines in production. • Ensure system reliability, performance optimization, and responsible deployment of ML and LLM solutions in production. • Manage the full model lifecycle, including retraining, performance monitoring, and continuous improvement. • Partner with Product, Engineering and Business stakeholders to translate requirements into scalable AI/ML and GenAI solutions, contributing to system architecture and technical design. • Provide technical guidance and support on AI/ML initiatives, delivering clear, actionable insights to stakeholders. • Contribute to AI engineering standards and best practices across the organization. • Create and maintain comprehensive documentation for ML and GenAI systems, including prompt libraries, evaluation frameworks, and architectural decisions. • Establish and promote best practices across MLOps, LLMOps and AI system governance.