Key requirements: - Bachelor’s or Master’s degree in computer science, AI/ML, engineering, or a technical field. - 5–10 years of experience managing software projects for global teams, including Enterprise AI projects. - Strong technical background with experience in software development processes, requirements management, and change management. - Demonstrated leadership, decision-making, teamwork, interpersonal and communication skills, with a track record of executing in large, multi-discipline teams across multiple time zones. - Proficiency with MS Project, Confluence, and JIRA for planning, execution, and reporting. - Understanding of the AI/ML lifecycle, data acquisition, model development, training, validation, deployment, and monitoring; knowledge of model performance metrics and retraining strategies. - Ability to work with diverse disciplines such as AI researchers, data scientists, ML engineers, software engineers, and business stakeholders. - Awareness of risks unique to AI software and experience defining success criteria, validation processes,

Key requirements: - Bachelor’s or Master’s degree in computer science, AI/ML, engineering, or a technical field. - 5–10 years of experience managing software projects for global teams, including Enterprise AI projects. - Strong technical background with experience in software development processes, requirements management, and change management. - Demonstrated leadership, decision-making, teamwork, interpersonal and communication skills, with a track record of executing in large, multi-discipline teams across multiple time zones. - Proficiency with MS Project, Confluence, and JIRA for planning, execution, and reporting. - Understanding of the AI/ML lifecycle, data acquisition, model development, training, validation, deployment, and monitoring; knowledge of model performance metrics and retraining strategies. - Ability to work with diverse disciplines such as AI researchers, data scientists, ML engineers, software engineers, and business stakeholders. - Awareness of risks unique to AI software and experience defining success criteria, validation processes,