1. 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.

2. 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.

3. 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.

4. 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.

5. 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.