Oversee and perform a diverse range of tasks and obligations, ensuring adherence to established protocols and objectives. Manage day-to-day operations with precision, prioritizing efficiency and accuracy in all deliverables. Collaborate closely with cross-functional teams to align efforts toward common goals, fostering a culture of accountability and continuous improvement. Maintain meticulous documentation, monitor performance metrics, and address any deviations promptly to uphold operational standards. Develop and implement process enhancements to optimize workflows, reduce redundancies, and drive productivity. Provide regular updates to leadership, offering strategic insights and recommendations based on data-driven analysis. Ensure compliance with industry regulations, company policies, and ethical guidelines to mitigate risks and maintain organizational integrity.
Developing Flops frameworks and automating pipelines to streamline machine learning workflows and enhance operational efficiency.
Develop and deploy comprehensive CI/CD pipelines alongside robust, scalable Flops frameworks to streamline development, testing, and deployment processes.
Design, implement, and manage robust data, training, and deployment pipelines with a focus on ensuring reproducibility, scalability, and operational efficiency.
Our responsibilities encompass the deployment of models, ongoing monitoring of their performance, and continuous optimization to enhance overall efficiency.
Deploy machine learning models into production environments while maintaining robust operational performance and reliability.
Design and deploy monitoring, logging, and alerting frameworks to continuously assess model performance and detect drift in accuracy.
Image-Based AI and Digital Phenotypic Solutions specializes in developing cutting-edge technologies that leverage artificial intelligence to analyze visual data and extract meaningful insights. The role requires a strong foundation in computer vision, machine learning, and data science, with proficiency in tools such as Python, TensorFlow, and PyTorch. Responsibilities include designing, implementing, and optimizing AI models for image recognition, segmentation, and classification tasks, as well as collaborating with cross-functional teams to integrate these solutions into scalable digital health platforms. Candidates should possess a minimum of a Bachelor’s degree in Computer Science, Engineering, or a related field, along with at least three years of hands-on experience in AI-driven image processing and digital phenotypic applications.
Facilitate the advancement and implementation of image recognition models by leveraging drone and mobile imagery for enhanced data processing and model training.
Leverage platforms like Roboflow and Databricks to execute image-based workflows and facilitate scalable machine learning operations.
Collaborates seamlessly across departments and institutions, fostering cohesive partnerships to align goals and enhance collective outcomes. Works closely with internal teams and external stakeholders to integrate diverse perspectives, ensuring cohesive strategies and streamlined processes that drive institutional success.
Collaborate with CGIAR partners, including ICRISAT and IATA, as well as internal teams to align and standardize Machine Learning Operations (Flops) methodologies and processes.
Facilitate knowledge sharing and integration across multidisciplinary teams.
We are seeking a professional to oversee governance initiatives, enhance organizational capacity, and drive continuous improvement efforts. The role requires expertise in developing robust governance frameworks, strengthening institutional capabilities, and implementing sustainable enhancements to processes and systems. Responsibilities include establishing effective governance structures, fostering skill development within the organization, and identifying opportunities for ongoing progress. Candidates should possess strong leadership skills, a deep understanding of capacity-building strategies, and a commitment to excellence in continuous improvement practices.
Uphold adherence to established data governance, security, and privacy regulations while ensuring all processes align with organizational policies and legal requirements.
Provide training and promote adoption of best practices while integrating emerging MLOps.
Requirements
A Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Agricultural Informatics, or a closely related quantitative discipline is required.
Seeking candidates with a minimum of one to three years of hands-on experience in machine learning, data science, or Flops settings.
A proven comprehension of machine learning workflows, encompassing data preprocessing, model training, evaluation, deployment, and ongoing monitoring, is required.
Proficiency in developing and implementing machine learning models, deep learning frameworks, and Large Language Models (LLMs) within both research and production environments is required.
Proficiency in international research organizations, CGIAR centers, or agricultural research initiatives constitutes a valuable asset to this role.
Seeking individuals with a strong academic background, relevant expertise, and hands-on experience in the field. Candidates must possess a solid educational foundation, specialized knowledge, and a proven track record of practical application in the role’s requirements.
A bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Agricultural Informatics, or a closely related quantitative discipline is required.
Candidates should possess a minimum of one to three years of hands-on experience in machine learning, data science, or Flops-centric roles.
Individuals should possess a well-established grasp of machine learning processes, encompassing data preparation, algorithm training, performance assessment, implementation, and ongoing maintenance.
Proficiency in utilizing machine learning models, deep learning frameworks, and Large Language Models (LLMs) within both research-oriented and production-based environments is required.
Proficiency in international research frameworks, particularly within CGIAR centers or agricultural research initiatives, is considered a valuable asset for this role.
Terms and conditions
This role is classified as a Locally Recruited Staff (LRS) position. CINOR-ICRAF provides a competitive compensation package aligned with individual experience and qualifications. Employment contracts in Kenya are issued under ICRAF, adhering to Kenyan labor regulations. Eligible benefits for locally recruited staff include a non-contributory pension scheme, full medical coverage, paid annual and statutory leave, as well as access to learning and professional development programs in accordance with CINOR-ICRAF policies.
The position is initially offered for a duration of two years, which encompasses a six-month probationary period, with the potential for extension subject to satisfactory performance, ongoing alignment with organizational needs, and resource availability.
The position will be located in Nairobi, Kenya.
All applications will receive confirmation receipt; however, only shortlisted candidates will be contacted further.
Position Closes on: June 11, 2026
go to method of application
To initiate your application, kindly utilize the provided link(s) and proceed to the company’s official website.
Qualifications
BA/BSc/HND
Experience Required
1 - 3 years
