1. MLOps Framework Development and
Pipeline Automation
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Design and implement CI/CD pipelines
and scalable MLOps frameworks.
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Develop and maintain data, training,
and deployment pipelines ensuring reproducibility and efficiency.
2. Model Deployment, Monitoring, and Performance
Optimization
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Deploy machine learning models into
production and ensure reliable performance.
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Implement monitoring, logging, and
alerting systems to track model accuracy and drift.
3. Image-Based AI and Digital Phenotyping Solutions
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Support development and deployment of
image recognition models using drone and mobile imagery.
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Utilize tools such as Roboflow and
Databricks for image-based workflows and scalable ML operations.
4. Collaboration and Cross-Institutional Integration
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Work with CGIAR partners (e.g.,
ICRISAT, IITA) and internal teams to harmonize MLOps practices.
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Facilitate knowledge sharing and
integration across multidisciplinary teams.
5. Governance, Capacity Building, and Continuous Improvement
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Ensure compliance with data
governance, security, and privacy standards.
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Provide training and promote adoption
of best practices while integrating emerging MLOps.
Requirements
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Bachelor’s degree in Computer Science,
Data Science, Artificial Intelligence, Software Engineering, Agricultural
Informatics, or a related quantitative field.
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Minimum 1–3 years of relevant
experience in machine learning, data science, or MLOps environments.
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Demonstrated understanding of machine
learning workflows, including data preprocessing, model training, evaluation,
deployment, and monitoring.
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Experience
working with machine learning models, deep learning frameworks, and Large
Language Models (LLMs) in research or production settings.
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Experience working within
international research organizations, CGIAR centers, or agricultural research
projects will be an added advantage.
