Position overview

CHAI currently seeks an Entry-level Platform 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 engineer 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. S/He 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 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 incumbent 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. Platform Infrastructure Support

• Assist in deploying and maintaining AI platform infrastructure components (cloud, hybrid, and/or on-prem) under the guidance of senior engineers.
• Support Infrastructure-as-Code (IaC) tasks and assist in automated environment provisioning for AI and data platforms.
• Help maintain environment separation for research, staging, and production systems.
• Assist with routine backup procedures and support business continuity activities for AI-supported services.

2. MLOps & AI Lifecycle Support

• Support the operation of MLOps platform components, including model training runs,
  validation checks, and deployment tasks.
• Assist in maintaining model versioning records and audit trail documentation.
• Help monitor model and data pipeline performance, flagging anomalies or drift indicators to senior engineers.
• Support deployment and basic troubleshooting of AI inference services integrated into health systems.

3. Data Pipelines & Feature Infrastructure Support

• Assist in building and maintaining batch and streaming data pipelines under senior engineer
  supervision.
• Support data labeling workflows, dataset versioning, and lineage tracking activities.
• Help ensure data pipeline tasks meet documentation and traceability requirements.
• Perform routine pipeline monitoring and escalate issues as needed.

4. Security, Compliance & Reliability Support

• Assist in implementing access control configurations and audit logging for AI-supported systems.
• Support ongoing compliance activities related to health data governance and privacy requirements.
• Help maintain safe-fail and fallback configurations to ensure service continuity during AI disruptions.
• Participate in platform security assessments and routine vulnerability checks.

5. CI/CD & Automation Support

• Support the operation of CI/CD and GitOps workflows for platform services, data pipelines, and AI models.
• Assist with automated testing, security scanning steps, and deployment checklists.
• Help enforce code and configuration promotion through established auditable pipelines.

6. Resource & Cost Monitoring

• Assist in monitoring AI compute resource usage, including scheduled jobs and workload queues.
• Support cost tracking and resource utilization reporting under senior engineer direction.
• Help identify inefficiencies in compute usage for optimization.

7. Digital Health Systems Integration Support

• Assist with integration tasks connecting AI services to EMRs, HMIS, and laboratory or registry platforms.
• Support data exchange testing and help troubleshoot interoperability issues.
• Help document integration workflows and data movement processes.

8. Team Collaboration & Enablement

• Support AI Engineers, Data Scientists, and Researchers by maintaining their development and experimentation environments.
• Participate in cross-functional team activities and contribute to platform design discussions.
• Assist with technical documentation and preparation of platform support materials.

9. Documentation & Continuous Learning

• Maintain up-to-date documentation for assigned platform components, pipelines, and
  procedures.
• Actively build skills in emerging platform and MLOps technologies relevant to the role.
• Contribute to knowledge-sharing within the engineering team.

Required Qualifications

• High school certificate or diploma; a certificate or diploma from a recognized technical training institution is required.
• At least 1 year of hands-on experience in software engineering, DevOps, or a related
  technical role preferably in a health-related field
• Basic familiarity with containerization tools such as Docker and foundational concepts of
  Kubernetes.
• Exposure to GitOps principles and version control workflows (e.g., Git).
• Basic understanding of networking concepts, system monitoring, and security best practices.
• Working knowledge of 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.
• Clear communication skills, both written and verbal.
• Commitment to data quality and accuracy.

Nice-to-Have Skills

• Familiarity with infrastructure-as-code concepts (e.g., Ansible, Terraform).
• Exposure to ETL tools such as Apache NiFi, Talend, or Airflow.
• Basic exposure to cloud platforms (AWS, Google Cloud, or Azure).
• Familiarity with big data frameworks (e.g., Hadoop, Apache Spark, Kafka).
• Knowledge of non-relational databases (e.g., MongoDB).
• Familiarity with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery).
• Experience supporting on-premises data environments.

• Assist in deploying and maintaining AI platform infrastructure components (cloud, hybrid, and/or on-prem) under the guidance of senior engineers.
• Support Infrastructure-as-Code (IaC) tasks and assist in automated environment provisioning for AI and data platforms.
• Help maintain environment separation for research, staging, and production systems.
• Assist with routine backup procedures and support business continuity activities for AI-supported services.
• Support the operation of MLOps platform components, including model training runs, validation checks, and deployment tasks.
• Assist in maintaining model versioning records and audit trail documentation.
• Help monitor model and data pipeline performance, flagging anomalies or drift indicators to senior engineers.
• Support deployment and basic troubleshooting of AI inference services integrated into health systems.
• Assist in building and maintaining batch and streaming data pipelines under senior engineer supervision.
• Support data labeling workflows, dataset versioning, and lineage tracking activities.
• Help ensure data pipeline tasks meet documentation and traceability requirements.
• Perform routine pipeline monitoring and escalate issues as needed.
• Assist in implementing access control configurations and audit logging for AI-supported systems.
• Support ongoing compliance activities related to health data governance and privacy requirements.
• Help maintain safe-fail and fallback configurations to ensure service continuity during AI disruptions.
• Participate in platform security assessments and routine vulnerability checks.
• Support the operation of CI/CD and GitOps workflows for platform services, data pipelines, and AI models.
• Assist with automated testing, security scanning steps, and deployment checklists.
• Help enforce code and configuration promotion through established auditable pipelines.
• Assist in monitoring AI compute resource usage, including scheduled jobs and workload queues.
• Support cost tracking and resource utilization reporting under senior engineer direction.
• Help identify inefficiencies in compute usage for optimization.
• Assist with integration tasks connecting AI services to EMRs, HMIS, and laboratory or registry platforms.
• Support data exchange testing and help troubleshoot interoperability issues.
• Help document integration workflows and data movement processes.
• Support AI Engineers, Data Scientists, and Researchers by maintaining their development and experimentation environments.
• Participate in cross-functional team activities and contribute to platform design discussions.
• Assist with technical documentation and preparation of platform support materials.
• Maintain up-to-date documentation for assigned platform components, pipelines, and procedures.
• Actively build skills in emerging platform and MLOps technologies relevant to the role.
• Contribute to knowledge-sharing within the engineering team.

• Basic familiarity with containerization tools such as Docker and foundational concepts of Kubernetes.
• Exposure to GitOps principles and version control workflows (e.g., Git).
• Basic understanding of networking concepts, system monitoring, and security best practices.
• Working knowledge of 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.
• Clear communication skills, both written and verbal.
• Commitment to data quality and accuracy.

• High school certificate or diploma; a certificate or diploma from a recognized technical training institution is required.
• At least 1 year of hands-on experience in software engineering, DevOps, or a related technical role preferably in a health-related field
• Basic familiarity with containerization tools such as Docker and foundational concepts of Kubernetes.
• Exposure to GitOps principles and version control workflows (e.g., Git).
• Basic understanding of networking concepts, system monitoring, and security best practices.
• Working knowledge of 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.
• Clear communication skills, both written and verbal.
• Commitment to data quality and accuracy.