Overview

The Clinton Health Access Initiative, Inc. (CHAI) is a global health organization committed to saving lives and reducing the burden of disease in low-and middle-income countries while strengthening the capabilities of governments and the private sector in those countries to create and sustain high-quality health systems that can succeed without our assistance. For more information, please visit: http://www.clintonhealthaccess.org

The Government of Rwanda (GoR) strives to achieve Universal Health Coverage (UHC) by enabling equitable access to quality health services for the population as outlined in the Fourth Health Sector Strategic Plan (HSSP IV). This commitment has been made against a backdrop of rising health care costs and uncertainty around the future availability of donor funding and calls for improved health sector sustainability. Acknowledging this, the Government aims to employ effective, equitable, efficient, and sustainable health care financing and systems strengthening, putting an emphasis on primary health care as the cornerstone of a strong and sustainable health system.

CHAI Rwanda’s Health System Strengthening (HSS) programs work on a day-to-day basis with Government leadership in the Ministry of Health (MoH) and the Rwanda Social Security Board (RSSB). The goal of those programs is to support the Government to accelerate progress towards UHC by ensuring the sustainable delivery of quality, essential health services. This includes work to design and implement national Primary Health Care (PHC) reforms from health center level down to community health, to increase health workforce availability, to prioritize and sustainably finance essential services, and to support improved management, financing, and delivery of quality primary health care.

Position overview

CHAI currently seeks two Platform and DevOps Engineers to work with the Ministry of Health (MOH) National Health Intelligence Center to support the design, deployment, and maintenance of systems that collect, store, and analyze large sets of structured and unstructured data. The role involves assisting with data pipelines, databases, and big data tools, while also supporting DevOps practices such as CI/CD pipelines, containerization with Docker, and orchestration with Kubernetes. The engineers will contribute to system monitoring, troubleshooting, infrastructure maintenance, and automation to ensure that data systems are scalable, reliable, and optimized for use by data scientists, analysts, and other stakeholders. The candidates will be seconded to the National Health Intelligence Center (NHIC) and will report in parallel to CHAI, Program manager, Digital Health for specific CHAI-supported initiatives.

Platform Engineers will provide need-based technical assistance during the review and implementation of a new data analytics architecture at MOH/NHIC. This effort is a cornerstone to MOH’s goal to disrupt how data is managed and used, including big data, to inform important policy and operational decisions at all levels of implementation.

The Platform Engineers will help design and implement the framework for improved data architecture, governance and build capacity within the MOH and the National Health Intelligence Center (NHIC). In addition, the incumbents will work closely with the NHIC and digital team at MOH to incorporate and translate data needs into system requirements.

Job Description:

The key functions and deliverables of this role will include:

1. National AI & Platform Infrastructure Engineering
   Design, build, and operate highly available, scalable, and secure national AI platform infrastructure (cloud, hybrid, and/or on-prem).
   Lead Infrastructure-as-Code (IaC) and automated environment provisioning for all AI and data platforms.
   Ensure strong environment separation for research, staging, and regulated production systems.
   Implement disaster recovery, failover, backups, and national business continuity for AI-supported services.
2. MLOps & AI Lifecycle Operations
   Own and operate the end-to-end MLOps platform, supporting model training, validation, secure promotion to production, rollback and retirement
   Enforce model versioning, reproducibility standards, audit trails for clinical and public health AI
   Implement continuous performance monitoring, data and model drift detection, automated retraining workflows
   Support secure real-time and batch AI inference services integrated into operational health systems.
3. Data Platform, Pipelines & Feature Infrastructure
   Design and manage national-scale data platforms supporting structured and unstructured health data.
   Build and maintain batch and streaming data pipelines, AI training pipelines, feature engineering and feature store infrastructure
   Enforce dataset versioning, lineage tracking, labeling workflows
   Ensure all AI data pipelines meet clinical audit, research governance, and regulatory traceability standards.
4. Clinical-Grade Reliability, Security & Compliance
   Implement secure access control, encryption, inference traceability, patient-level audit logging for AI-supported clinical systems
   Ensure continuous compliance with national health data governance, privacy and cybersecurity regulations
   Implement safe-fail mechanisms so that clinical services remain functional in the event of AI service disruptions.
   Conduct ongoing platform security hardening, vulnerability assessment, and threat monitoring.
5. Continuous Delivery, GitOps & Automation
   Design and operate secure CI/CD and GitOps workflows for platform services, Data pipelines and AI models
   Implement automated testing, security scanning, deployment approvals and production release controls
   Enforce code, configuration, and model promotion through auditable pipelines only.
6. Performance, Cost & Resource Governance
   Manage national AI compute infrastructure including GPU clusters and Distributed training environments
   Implement Fair resource scheduling, Quotas, Cost monitoring and Government vs partner workload separation
   Continuously optimize platform cost efficiency, compute utilization and inference performance at national scale
7. Interoperability with National Digital Health Systems
   Enable secure, high-performance AI integration with EMRs, HMIS, Surveillance systems and Laboratory and registry platforms
   Ensure standards-based data exchange, reliable real-time and batch data movement and Interoperable AI services within care and reporting workflows
8. Technical Collaboration & Enablement
   Provide standardized, secure AI development and experimentation environments for AI Engineers, Data Scientists and Researchers
   Work closely with AI Engineers, Senior Data Scientists and Digital Health & Integration Teams
   Support technical proposals, platform design reviews and national AI deployments
9. Documentation, Knowledge Management & Continuous Improvement
   Maintain comprehensive documentation for platform architecture, MLOps workflows, Data pipelines and security controls
   Continuously assess emerging technologies that enhance platform resilience, AI operational safety and National scalability

Required Qualifications:

• Bachelor’s degree/Diploma in Computer Science, Information Systems, Engineering, or a related field (or equivalent experience).
• Proven experience in software engineering and/or DevOps practices for 4 years at least.
• Familiarity with Kubernetes, Docker, GitOps, and infrastructure-as-code (e.g., Ansible, Terraform).
• Basic understanding of networking concepts, system monitoring, and security best practices.
• Proficiency in SQL and experience with relational databases such as PostgreSQL.
• Strong problem-solving skills and attention to detail.
• Ability to collaborate

• Design, build, and operate highly available, scalable, and secure national AI platform infrastructure (cloud, hybrid, and/or on-prem).
• Lead Infrastructure-as-Code (IaC) and automated environment provisioning for all AI and data platforms.
• Ensure strong environment separation for research, staging, and regulated production systems.
• Implement disaster recovery, failover, backups, and national business continuity for AI-supported services.
• Own and operate the end-to-end MLOps platform, supporting model training, validation, secure promotion to production, rollback and retirement
• Enforce model versioning, reproducibility standards, audit trails for clinical and public health AI
• Implement continuous performance monitoring, data and model drift detection, automated retraining workflows
• Support secure real-time and batch AI inference services integrated into operational health systems.
• Design and manage national-scale data platforms supporting structured and unstructured health data.
• Build and maintain batch and streaming data pipelines, AI training pipelines, feature engineering and feature store infrastructure
• Enforce dataset versioning, lineage tracking, labeling workflows
• Ensure all AI data pipelines meet clinical audit, research governance, and regulatory traceability standards.
• Implement secure access control, encryption, inference traceability, patient-level audit logging for AI-supported clinical systems
• Ensure continuous compliance with national health data governance, privacy and cybersecurity regulations
• Implement safe-fail mechanisms so that clinical services remain functional in the event of AI service disruptions.
• Conduct ongoing platform security hardening, vulnerability assessment, and threat monitoring.
• Design and operate secure CI/CD and GitOps workflows for platform services, Data pipelines and AI models
• Implement automated testing, security scanning, deployment approvals and production release controls
• Enforce code, configuration, and model promotion through auditable pipelines only.
• Manage national AI compute infrastructure including GPU clusters and Distributed training environments
• Implement Fair resource scheduling, Quotas, Cost monitoring and Government vs partner workload separation
• Continuously optimize platform cost efficiency, compute utilization and inference performance at national scale
• Enable secure, high-performance AI integration with EMRs, HMIS, Surveillance systems and Laboratory and registry platforms
• Ensure standards-based data exchange, reliable real-time and batch data movement and Interoperable AI services within care and reporting workflows
• Provide standardized, secure AI development and experimentation environments for AI Engineers, Data Scientists and Researchers
• Work closely with AI Engineers, Senior Data Scientists and Digital Health & Integration Teams
• Support technical proposals, platform design reviews and national AI deployments
• Maintain comprehensive documentation for platform architecture, MLOps workflows, Data pipelines and security controls
• Continuously assess emerging technologies that enhance platform resilience, AI operational safety and National scalability

• Kubernetes
• Docker
• GitOps
• Infrastructure-as-Code (e.g., Ansible, Terraform)
• Networking concepts
• System monitoring
• Security best practices
• SQL
• Relational databases (e.g., PostgreSQL)
• Problem-solving
• Attention to detail
• Collaboration

• Bachelor’s degree/Diploma in Computer Science, Information Systems, Engineering, or a related field (or equivalent experience).
• Proven experience in software engineering and/or DevOps practices for 4 years at least.
• Familiarity with Kubernetes, Docker, GitOps, and infrastructure-as-code (e.g., Ansible, Terraform).
• Basic understanding of networking concepts, system monitoring, and security best practices.
• Proficiency in SQL and experience with relational databases such as PostgreSQL.
• Strong problem-solving skills and attention to detail.
• Ability to collaborate