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, CamerooDuties and 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. 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. Education, knowledge and experience 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. Terms and conditions This is a Locally Recruited Staff (LRS) position. CIFOR-ICRAF offers a competitive remuneration package commensurate with experience and qualifications. In Kenya, employment contracts are issued under ICRAF in compliance with Kenyan labour law. Locally recruited staff are eligible for a non-contributory pension scheme, comprehensive medical insurance, paid annual and statutory leave, and learning and development opportunities in line with CIFOR-ICRAF policies. The appointment will be for a period of two (2) years, inclusive of a six-month probationary period, with the possibility of extension contingent upon performance, continued relevance of the position and available resources. The duty station will be in Nairobi, Kenya. We will acknowledge all applications but will only contact short-listed candidates. End Date: 11th June 2026