Lead the design, development, and deployment of novel agentic systems and core machine learning models that power AI-driven capabilities.
Execute data analysis, error analysis, and rigorous experimentation to drive model improvements and new capability development.
Design and implement the end-to-end machine learning pipeline (MLOps), ensuring model scalability, reliability, and consumption via robust APIs.
Work with large-scale datasets to perform data wrangling, feature engineering, and validation to train and fine-tune state-of-the-art models.
Apply machine learning and distributed systems principles in production to address model scalability, concurrency, fault tolerance, and performance challenges.
Own ML models and systems through their full lifecycle, including deployment, monitoring, debugging, and ongoing operational improvements.