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.

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.

• Design and implement CI/CD pipelines and scalable MLOps frameworks.
• Develop and maintain data, training, and deployment pipelines ensuring reproducibility and efficiency.
• Deploy machine learning models into production and ensure reliable performance.
• Implement monitoring, logging, and alerting systems to track model accuracy and drift.
• 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.
• Work with CGIAR partners (e.g., ICRISAT, IITA) and internal teams to harmonize MLOps practices.
• Facilitate knowledge sharing and integration across multidisciplinary teams.
• Ensure compliance with data governance, security, and privacy standards.
• Provide training and promote adoption of best practices while integrating emerging MLOps.

• CI/CD pipelines
• MLOps frameworks
• Data, training, and deployment pipelines
• Model deployment
• Model monitoring
• Image recognition models
• Roboflow
• Databricks
• Data preprocessing
• Model training
• Model evaluation
• Deep learning frameworks
• Large Language Models (LLMs)
• Data governance
• Security standards
• Privacy standards

• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Agricultural Informatics, or a related quantitative field.
• 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.