1. MLOps Framework Development and Pipeline Automation

• Design and implement CI/CD pipelines and scalable MLOps frameworks.
  
• Develop and maintain data, training, and deployment pipelines ensuring reproducibility and efficiency.
  

2. Model Deployment, Monitoring, and Performance Optimization

• Deploy machine learning models into production and ensure reliable performance.
  
• Implement monitoring, logging, and alerting systems to track model accuracy and drift.
  

3. Image-Based AI and Digital Phenotyping Solutions

• Support development and deployment of image recognition models using drone and mobile imagery.
  
• Utilize tools such as Roboflow and Databricks for image-based workflows and scalable ML operations.
  

4. Collaboration and Cross-Institutional Integration

• Work with CGIAR partners (e.g., ICRISAT, IITA) and internal teams to harmonize MLOps practices.
  
• Facilitate knowledge sharing and integration across multidisciplinary teams.
  

5. Governance, Capacity Building, and Continuous Improvement

• Ensure compliance with data governance, security, and privacy standards.
  
• Provide training and promote adoption of best practices while integrating emerging MLOps.