Internal Call Only(RFP300452) - Senior Associate - Machine Learning OperationsArusha, Tanzania
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Internal Call Only(RFP300452) - Senior Associate - Machine Learning OperationsArusha, Tanzania
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Job Description
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.
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.
Collaboration with Engineering and Product Teams:
- Work closely with software engineers user research experts and product teams to integrate ML models into applications.
- Perform model testing and validating outputs for usability and accuracy.
- Provide technical support for model-related issues in production.
Requirements
- Masters 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).
- Familiarity with ML pipeline orchestration tools (e.g. Kubeflow MLflow Airflow).
- Understanding of CI/CD for ML deployment.
- Strong problem-solving skills and attention to detail.
- Excellent communication and interpersonal skills.
- Ability to work independently and as part of a team.
Terms of employment
This position is a nationally recruited position based in Arusha Tanzania. The initial contract will be for 1 year subject to a probation period of 3 months and is renewable depending on performance and availability of resources.
This position is graded at BG07 level with a minimum basic salary of TSZ 3929704 in a scale of BG01 to BG14 (BG14 being the highest level according to the Alliance job classification framework policy). We offer a competitive salary and excellent benefits including but not limited to insurance retirement plan staff training and development paid time off and flexible working arrangements.
Applications
Applications closing date: 20th June 2025
Please note that email applications will not be considered.
Only short-listed candidates will be contacted.
Employment Type
Full Time
