Senior Associate – Machine Learning Operations
2025-06-19T10:59:43+00:00
Bioversity International
https://cdn.greattanzaniajobs.com/jsjobsdata/data/employer/comp_3801/logo/Bioversity%20International.png
http://alliancebioversityciat.org
FULL_TIME
Arusha
Arusha
00000
Tanzania
Nonprofit, and NGO
Science & Engineering
2025-06-20T17:00:00+00:00
Tanzania
8
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.
- 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
- 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).
- 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 3,929,704 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.
The Alliance Bioversity-CIAT is committed to fair, safe, and inclusive workplaces. We believe that diversity powers our innovation, contributes to our excellence, and is critical for our mission. Recruiting and mentoring staff to create an inclusive organization that reflects our global character is a priority. We encourage applicants from all cultures, races, colors, religions, sexes, national or regional origins, ages, disability statuses, sexual orientations, marital status, and gender identities. Female candidates are strongly encouraged to apply
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. 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.
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.
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). Familiarity with ML pipeline orchestration tools (e.g., Kubeflow, MLflow, Airflow).
JOB-6853ed9fc9656
Vacancy title:
Senior Associate – Machine Learning Operations
[Type: FULL_TIME, Industry: Nonprofit, and NGO, Category: Science & Engineering]
Jobs at:
Bioversity International
Deadline of this Job:
Friday, June 20 2025
Duty Station:
Arusha | Arusha | Tanzania
Summary
Date Posted: Thursday, June 19 2025, Base Salary: Not Disclosed
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JOB DETAILS:
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.
- 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
- 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).
- 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 3,929,704 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.
The Alliance Bioversity-CIAT is committed to fair, safe, and inclusive workplaces. We believe that diversity powers our innovation, contributes to our excellence, and is critical for our mission. Recruiting and mentoring staff to create an inclusive organization that reflects our global character is a priority. We encourage applicants from all cultures, races, colors, religions, sexes, national or regional origins, ages, disability statuses, sexual orientations, marital status, and gender identities. Female candidates are strongly encouraged to apply
Work Hours: 8
Experience in Months: 36
Level of Education: postgraduate degree
Job application procedure
Applicants are invited to visit https://alliancebioversityciat.org/careers to get full details of the position and to submit their applications. Applications MUST include reference number RFP300452 as the position applied for. Cover letter and CV
should be saved as one document using the candidate’s last name, first name for ease of sorting. The Alliance collects and processes personal data in accordance with applicable data protection laws.
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