JOB VACANCY: TECHNICAL ADVISOR – AI ENGINEER (2 POSITIONS)

Organization: Clinton Health Access Initiative (CHAI) – Rwanda

Program: Digital Health
Duty Station: Kigali, Rwanda
Employment Type: Full-Time (Paid)
Start Date: Immediate

About CHAI

The Clinton Health Access Initiative, Inc. (CHAI) is a global health organization dedicated to saving lives and reducing the burden of disease in low- and middle-income countries. CHAI works closely with governments to strengthen health systems, improve access to essential medicines, and support sustainable healthcare delivery.

In Rwanda, CHAI partners with the Ministry of Health (MoH) and the Rwanda Social Security Board (RSSB) to advance Universal Health Coverage (UHC) through health system strengthening, primary health care reforms, workforce development, financing, and digital health innovation.

Position Overview

CHAI Rwanda is seeking two (2) highly skilled AI Engineers to support the Ministry of Health’s National Health Intelligence Center (NHIC) in the design, development, and deployment of artificial intelligence (AI) solutions that strengthen Rwanda’s health system.

The AI Engineers will contribute to the development of scalable, secure, and interoperable AI systems that transform health data into actionable insights for decision-making, service delivery, and public health planning. The role focuses on production-grade AI system development, MLOps, integration with national digital health platforms, and clinical validation.

The successful candidates will be seconded to NHIC and report to the NHIC/Health AI Lead and the CHAI Digital Health Program Manager, working closely with data scientists and digital health teams.

Key Responsibilities

1. AI Model Development & Optimization

• Design, develop, train, and optimize machine learning and deep learning models.

• Perform data preprocessing, feature engineering, model validation, tuning, and optimization.

• Develop explainable AI pipelines to support clinical trust and regulatory transparency.

• Apply best practices in version control, experiment tracking, and reproducible ML workflows.

2. Deployment, MLOps & Monitoring

• Deploy AI models into secure and scalable production environments.

• Establish monitoring systems for model performance, data drift, and concept drift.

• Implement automated retraining, rollback mechanisms, and release management.

• Optimize inference performance, reliability, and cost efficiency.

3. System Integration & Interoperability

• Integrate AI solutions with national digital health platforms (EMRs, HMIS, LMIS).

• Implement interoperability standards using APIs, HL7/FHIR, and MoH-recommended architectures.

• Develop secure real-time and batch data pipelines for AI inference.

4. Data Management, Quality & Security

• Collaborate with NHIC data teams to manage structured and unstructured datasets.

• Ensure data quality, bias detection, and representativeness checks.

• Implement secure data handling, encryption, access controls, and audit logging.

• Maintain dataset documentation and data lineage.

5. Model Evaluation, Clinical Validation & Regulatory Support

• Conduct rigorous model testing for accuracy, fairness, and clinical relevance.

• Support clinical pilots and facility-level validation.

• Prepare technical documentation for ethics, regulatory, and audit reviews.

• Perform error analysis and continuous model improvement.

6. Documentation & Knowledge Sharing

• Produce comprehensive technical documentation for AI systems and workflows.

• Contribute to reports, concept notes, donor updates, and system design briefs.

• Support development of user guides, SOPs, and training materials.

7. Collaboration & Technical Advisory

• Collaborate with health sector stakeholders and digital health teams.

• Participate in AI technical working groups and review sessions.

• Provide AI engineering input for grants, research initiatives, and partnerships.

8. Continuous Learning & Innovation

• Stay updated on emerging AI technologies and tools.

• Propose innovative, high-impact AI use cases for the health sector.

• Support continuous improvement of national AI infrastructure.

Required Qualifications

Education

• Master’s degree or higher in Artificial Intelligence, Computer Engineering, Data Science, Biomedical Engineering, Health Informatics, or a closely related field.

Experience

• 4–5 years of experience in applied machine learning and production-grade AI systems.

• Proven experience deploying and maintaining AI or healthcare IT systems in real-world environments.

Technical Skills

• Strong experience with APIs, data pipelines, ML frameworks, and system integration.

• Hands-on experience across the full Large Language Model (LLM) lifecycle.

• Advanced knowledge of reinforcement learning techniques.

• Experience designing scalable model evaluation frameworks.

• Strong engineering skills for deployment, monitoring, and maintenance of AI systems.

How to Apply

Interested candidates should by Clicking to APPLY BUTTON

📌 Only shortlisted candidates will be contacted.