Senior Executive, AI Operations

- Model Deployment: Automate and streamline the process of deploying machine learning models into production environments, ensuring they run reliably at scale. - Pipeline Construction: Build and maintain robust data pipelines for continuous training and deployment of machine learning models, including Generative AI models such as LLMs (Large Language Models). - Monitoring and Maintenance: Implement monitoring solutions to track the performance and health of models in production, quickly identifying and addressing degradation or failures. - Versioning and Experiment Tracking: Manage version control of both data and models. Use tools like ML flow or DVC to track experiments, manage the lifecycle of machine learning models, and ensure reproducibility. - Collaboration: Work closely with data scientists, AI researchers, and software engineers to ensure that ML systems are well-integrated with the company’s software infrastructure. - Performance Optimization: Optimize machine learning infrastructure for performance and cost, utilizing cloud technologies and services efficiently. - Regulatory Compliance: Ensure that the machine learning deployments comply with relevant data privacy and protection regulations, particularly when handling sensitive or personal data. - Best Practices and Standards: Establish and promote ML Ops best practices within the team, including guidelines for code quality, deployment procedures, and security measures