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