The Center for International Forestry Research (CIFOR) is a non-profit, scientific institution that conducts research on the most pressing challenges of forest and landscape management around the world. Using a global, multidisciplinary approach, we aim to improve human well-being, protect the environment, and increase equity. To do so, we conduct innovative research, develop partners’ capacity, and actively engage in dialogue with all stakeholders to inform policies and practices that affect forests and people. CIFOR is a CGIAR Research Center, and leads the CGIAR Research Program on Forests, Trees and Agroforestry (FTA). Our headquarters are in Bogor, Indonesia, with offices in Nairobi, Kenya; Yaounde, Cameroon

Responsibilities or duties

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.

Qualifications or requirements 

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

Experience needed

• 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.

• Machine learning
• Data science
• MLOps
• CI/CD pipelines
• Scalable MLOps frameworks
• Data pipelines
• Training pipelines
• Deployment pipelines
• Model deployment
• Model monitoring
• Model performance optimization
• Image recognition models
• Drone imagery
• Mobile imagery
• Roboflow
• Databricks
• Data governance
• Security standards
• Privacy standards
• Deep learning frameworks
• Large Language Models (LLMs)

• 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.