Requirements

• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Agricultural Informatics, or a related quantitative field.
  
• Minimum 1–3 years of relevant experience in machine learning, data science, or MLOps environments.
  
• Demonstrated understanding of machine learning workflows, including data preprocessing, model training, evaluation, deployment, and monitoring.
  
• Experience working with machine learning models, deep learning frameworks, and Large Language Models (LLMs) in research or production settings.
  
• Experience working within international research organizations, CGIAR centers, or agricultural research projects will be an added advantage.
  

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.
  

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.
  

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.
  

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.
  

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