Design and Implement Models: Research, develop, and deploy advanced machine learning and deep learning models for high-impact, real-world applications.
Drive the ML Lifecycle: Own the end-to-end process from initial data exploration and high-quality dataset creation to model training, tuning, validation, and production deployment.
Establish MLOps: Implement robust MLOps practices, including Continuous Integration/Continuous Deployment (CI/CD), automated monitoring, containerization (e.g. With Docker) and infrastructure automation to ensure model reliability and scalability.
Drive Improvement & Learning: Focus on prompt engineering, quantitative output evaluation and data labeling strategies. Create strategies to assure high quality model outputs.
Collaborate and Integrate: Partner closely with Product Managers, Software Engineers, and QA teams to sea...