World Agroforestry Centre (ICRAF)
Data/Research
Description
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.
Responsibilities
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
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