Machine Learning and Operations Consultancy - Computer Vision

THE ORGANIZATION

The Alliance of Bioversity International (

www.bioversityinternational.org

) and the International Center for Tropical Agriculture (CIAT) (

www.ciat.cgiar.org

) 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.

With novel partnerships, the Alliance generates evidence and mainstreams innovations to transform food systems and landscapes so that they sustain the planet, drive prosperity, and nourish people in a climate crisis.

The Alliance is part of CGIAR, a global research partnership for a food-secure future.

Background:

The Alliance of Bioversity International and CIAT leads cutting-edge initiatives leveraging computer vision for plant phenotyping. The objective is to design, validate, and deploy robust computer vision phenotyping systems capable of operating seamlessly across mobile phones, uncrewed aerial vehicles (drones), and rovers within breeding centers across the CGIAR network. By harnessing artificial intelligence and advanced digital phenotyping, we aim to modernize conventional breeding approaches and accelerate the development of high-yielding, climate-adapted seed varieties of staple food crops.

About the positions:

The consultants will support end-to-end MLOps workflows spanning data ingestion, validation, dataset versioning, model training, evaluation, deployment, monitoring, and continuous improvement across both cloud and edge environments. The roles will involve close collaboration with research machine learning, software engineering, product, and field teams to ensure systems are robust, maintainable, and aligned with project needs.

The consultants will also support the integration of ML systems within the ONA platform, a dedicated web- and smartphone-based platform for computer vision phenotyping, including deployment workflows connecting mobile applications, cloud infrastructure, visual data pipelines, disease detection and severity scoring workflows, backend services, and FAIRGrounds-integrated systems. In addition, the roles will contribute to strengthening best practices around experiment tracking, model governance, CI/CD workflows, deployment automation, and ML system monitoring across ONA infrastructure.

These are 11-month full-time consultancy positions (with potential for extension) based at the Alliance office in Arusha, Tanzania or Nairobi, Kenya.

Key Activities and Specific Terms of Reference

Computer Vision Systems Development and Optimization

• Support the development, fine-tuning, and optimization of robust, high-accuracy computer vision models for crop phenotyping across diverse field conditions.

• Develop and maintain automated workflows for image data preprocessing, quality assurance, feature engineering, and annotation curation.

• Conduct systematic hyperparameter tuning, model validation experiments, and benchmarking against baseline datasets.

• Support the containerization and optimization of inference workflows for low-connectivity, edge, and resource-constrained environments (e.g., mobile devices, edge compute).

• Monitor production model performance, latency, throughput, and data/concept drift under variable field lighting and environmental conditions.

Machine Learning Pipeline Development and Maintenance

• Develop, automate, and maintain scalable end-to-end ML pipelines for data ingestion, training, validation, and deployment.

• Support the implementation of CI/CD pipelines to streamline automated model updates, testing, and deployment.

• Establish and maintain best practices for version control of datasets, code, and model artifacts to guarantee full reproducibility.

• Collaborate with data engineers, software engineers, and product teams to integrate ML models into operational production applications.

Disease Detection and Severity Scoring

• Support development and deployment of AI workflows for disease detection and severity scoring using field images and visual data.

• Implement data and evaluation pipelines for disease annotation, validation, benchmarking, and continuous model improvement.

• Support integration of disease scoring workflows within the ONA platform for field-based data collection and analysis.

MLOps Infrastructure and ONA Integration

• Develop CI/CD pipelines for model training, evaluation, and deployment.

• Manage experiment tracking, model registries, and dataset versioning workflows.

• Implement monitoring and logging across ML services.

• Support deployment of ML services on GCP, AWS, and related cloud/edge infrastructure.

• Support integration of ML services within the ONA platform across mobile, backend, API, and cloud systems.

• Ensure compliance with data governance, security, and responsible AI requirements.

Deliverables and Payment Schedule:

•

Deliverable 1:

Inception Report and Technical Workplan (Month 1, ~Week 4)

Develop an inception report outlining the technical approach, deployment priorities, infrastructure requirements, integration roadmap, and detailed 11-month workplan for MLOps, computer vision, and disease-scoring workflows across the ONA platform.

Honoraria for Deliverable 1: 12,000,000 Tanzanian Shillings / 589,762 Kenyan Shillings

•

Deliverable 2:

Implementation of Ingestion, Annotation Selection, Model Development, and Evaluation Pipelines (Month 5, ~Week 20)

Implement operational workflows and data pipelines focusing on data ingestion, annotation selection, model training, benchmarking, and evaluation across cloud and edge environments, building on established core MLOps infrastructure.

Honoraria for Deliverable 2: 18,000,000 Tanzanian Shillings / 884,642 Kenyan Shillings

•

Deliverable 3:

ONA AI Pipeline Integration, Breeding Team Support, and Trait Model Deployment (Month 8, ~Week 32)

Deliver integrated deployment workflows connecting ML/CV services with ONA mobile applications, APIs, backend systems, and FAIRGrounds-integrated infrastructure. Assist breeding teams with advanced ML/CV tasks, operationalizing and deploying trait extraction models into production alongside monitoring and evaluation documentation.

Honoraria for Deliverable 3: 38,000,000 Tanzanian Shillings / 1,867,579 Kenyan Shillings

•

Deliverable 4:

Final Technical Report and Handover Package (Month 11, end of assignment)

Submit a final technical report summarizing completed workflows, deployed infrastructure, model performance across targets, key learnings, and recommendations for future scaling and maintenance. Deliver finalized documentation, deployment guides, pipeline configurations, and knowledge-transfer materials for internal teams.

Honoraria for Deliverable 4: 22,000,000 Tanzanian Shillings / 1,081,230 Kenyan Shillings

Requirements

• Education:
• • Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.
• Technical Competencies:
• • Experience building and managing ML workflows, including model training, deployment, monitoring, and versioning.
• • Strong programming skills in Python and familiarity with ML frameworks such as PyTorch or TensorFlow.
• • Experience working with cloud platforms such as GCP, AWS, or Azure.
• • Experience with computer vision, image-based AI workflows, or multimodal AI applications.
• • Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools.
• • Ability to work collaboratively across technical, product, and field teams.
• • Strong communication, documentation, and problem-solving skills.
• • Background or experience in agriculture, digital agriculture, or international research environments will be an added advantage.
• Benefits
• Terms of employment
• These are nationally recruited positions based in either Kenya or Tanzania. The initial contract will be for up to 11 Months, with the total estimated consultancy amount of 90,000,000 Tanzanian Shillings or 4,423,212 Kenyan Shillings.
• Applications
• Applicants are invited to visit
• https://www.bioversityinternational.org/jobs/
• to get full details of the position and to submit their applications. Applications MUST include reference number Ref: MACHINE LEARNING AND OPERATIONS CONSULTANCY (COMPUTER VISION) as the position applied for. Application including CV, technical proposal and financial proposal should be saved as one document using the candidate’s last name, first name for ease of sorting.
• Note: The Alliance does not charge a fee at any stage of the recruitment process (application, interview meeting, processing or training). The Alliance also does not concern itself with information on applicants' bank accounts.
• Applications closing date: 30 October 2026
• At Impactpool we do our best to provide you the most accurate info, but closing dates may be wrong on our site. Please check on the recruiting organization's page for the exact info. Candidates are responsible for complying with deadlines and are encouraged to submit applications well ahead.
• Before applying, please make sure that you have read the requirements for the position and that you qualify. Applications from non-qualifying applicants will most likely be discarded by the recruiting manager.
• Summary by Impactpool
• The Alliance of Bioversity International and CIAT is seeking consultants for machine learning and operations consultancy focused on computer vision in agriculture. The role involves developing and optimizing computer vision systems for crop phenotyping, managing ML pipelines, and integrating ML services within the ONA platform. Candidates will collaborate with various teams to ensure robust and maintainable systems while adhering to best practices in MLOps. This is an 11-month full-time consultancy position based in Arusha, Tanzania or Nairobi, Kenya.
• Candidate Requirements:
• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field
• Experience building and managing ML workflows
• Strong programming skills in Python
• Familiarity with ML frameworks like PyTorch or TensorFlow
• Experience with cloud platforms (GCP, AWS, Azure)
• Experience in computer vision or image-based AI workflows
• Familiarity with containerization technologies (Docker, Kubernetes)
• Ability to work collaboratively across teams
• Strong communication and problem-solving skills
• Background in agriculture or digital agriculture is a plus
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