Officer, Platform & Artificial Intelligence (AI) Data Engineer

Job Description

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. As an Engineer, you 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's Program manager, Digital Health for specific CHAI-supported initiatives.

The Platform Engineer 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.

Key Functions and Deliverables

• 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.
• 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.
• 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.
• 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.
• 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.
• 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
• 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
• 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
• 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

Requirements

• Bachelor’s degree/Diploma in Computer Science, Information Systems, Engineering, or a related field (or equivalent experience).
• Minimum of 4 years of work experience with proven experience in software engineering and/or DevOps practices.
• 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 effectively with cross-functional teams.
• Strong communication skills, both written and verbal.
• Commitment to data quality and accuracy.