THE ORGANIZATION
The Alliance of Bioversity International and CIAT delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve people's lives. Alliance solutions address the global crises of malnutrition, climate change, biodiversity loss, and environmental degradation.
The Alliance works with local, national and multinational partners across Latin America and the Caribbean, Asia and Africa, and with the public and private sectors. The Alliance is part of CGIAR, a global research partnership for a food-secure future, dedicated to reducing poverty, enhancing food and nutrition security, and improving natural resources and ecosystem services.
About the position
The Senior Machine Learning Operations (MLOps) Associate will support the development, deployment, and maintenance of ML models and infrastructure within the Artemis project. This role focuses on assisting in ML pipeline development, automating workflows, managing model performance in production, and ensuring smooth integration into research and application environments. The Senior associate will work closely with the engineering and product development teams to support scalable, reliable, and well-documented ML operations.
Key duties & responsibilities
Model development:
- Oversee training and evaluation of robust, high-accurate models for crop phenotyping.
- Perform data preprocessing and feature engineering for model training.
- Conduct basic hyperparameter tuning and model validation experiments.
Machine Learning pipeline development and maintenance:
- Develop and maintain ML pipelines for training, validation, and deployment.
- Automate workflows for data preprocessing, model retraining, and evaluation.
- Ensure model artifacts are properly versioned and documented.
Model deployment and monitoring:
- Support the deployment of ML models in production environments.
- Set up monitoring tools to track model performance and detect drift.
- Optimize inference speed and resource usage for improved efficiency.
Infrastructure and CI/CD for ML
- Setup and maintain cloud-based and on-premise ML infrastructure.
- Support the implementation of CI/CD pipelines for automated model updates and deployment.
- Assist with containerization (Docker, Kubernetes) and model serving.
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Collaboration with Engineering and Product Teams:
- Work closely with software engineers, user research experts, and product teams to integrate ML models intoย ย ย ย ย applications.
- Perform model testing and validating outputs for usability and accuracy.
- Provide technical support for model-related issues in production.
Requirements
- Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in machine learning, data engineering, or related domains
- Proficient in data preprocessing, model training, evaluation, and optimization
- Experienced in deploying models to production environments and monitoring their performance.
- Proficiency in Python and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Basic experience with cloud platforms (GCP, AWS, Azure).
- Master's degree in Computer Science, Engineering, or a related field.
- Work closely with software engineers, user research experts, and product teams to integrate ML models intoย ย ย ย ย applications.
- Setup and maintain cloud-based and on-premise ML infrastructure.
- Support the deployment of ML models in production environments.
- Develop and maintain ML pipelines for training, validation, and deployment.