Research Specialist- Machine Learning Operations at Bioversity International

• The Research Specialist will lead the implementation and continuous improvement of Machine Learning Operations (MLOps) processes and infrastructure. They will collaborate with cross-functional teams to ensure the seamless deployment, monitoring, and maintenance of machine learning models in production environments.

Key duties & responsibilities

• Design and implement MLOps strategies and frameworks (CI/CD pipelines) to streamline the development, deployment, and monitoring of machine learning models.
• Collaborate with data scientists, software engineers, and DevOps teams to deploy and operationalize machine learning models in production environments.
•  Develop and maintain scalable and reliable pipelines for data preprocessing, feature engineering, model training, and model serving.
• Establish and maintain best practices for version control, model reproducibility, and model performance tracking.
• Implement and manage infrastructure for model monitoring, logging, and alerting to ensure the reliability and performance of deployed models.
• Automate testing and validation processes to ensure the accuracy and robustness of machine learning models.
• Collaborate with IT and security teams to ensure data privacy, compliance, and security standards are met throughout the MLOps lifecycle.
• Provide technical guidance and training to internal teams on MLOps practices and tools.
•  Stay up-to-date with the latest trends and advancements in the MLOps field.

Requirements

• Master's degree in Computer Science, Engineering, or a related field.
• A formal background in one or more of the following: Computer Science, Data Science, Software Engineering, Data Engineering, Statistics, Mathematics
• Strong hands-on experience with machine learning frameworks and tools such as TensorFlow, PyTorch, or scikit-learn.
• Proficiency in programming languages like Python, as well as experience with software development practices and version control systems.
• Solid understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and experience with deploying machine learning models in cloud environments.
• Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools.
• Knowledge of data engineering principles, including data preprocessing, feature engineering, and data pipeline development.
• Strong problem-solving skills and the ability to work in a collaborative environment.
• Ability to analyze data, identify patterns, and draw meaningful conclusions while ensuring accuracy and thoroughness in research and data collection.
• Ability to think outside the box to develop innovative research approaches and solutions.