M05 - DevSecOps Engineer

Responsibilities

The DevSecOps Engineer shall be responsible for the following:

• Own the operationalisation and deployment of ML models into production, working closely with the Data Scientist at the handoff between model development and deployment.
• Manage ML infrastructure and monitor model performance in production, ensuring models remain reliable and accurate over time.
• Own platform security and compliance, ensuring the platform meets IM8 compliance, Government Commercial Cloud (GCC) security baseline requirements, and other applicable data compliance requirements.
• Address data privacy requirements (e.g. PDPA), including implementing measures such as blurring of personally identifiable information in sensitive data sources (e.g. CCTV feeds).
• Manage the configuration, deployment, and operational readiness of the data platform on the GCC environment.
• Implement infrastructure security controls, ensuring infrastructure meets required GCC security baselines.
• Own data quality standards across the platform, ensuring data is complete, accurate, schema-conformant, and latency-compliant before it propagates downstream.
• Produce infrastructure-specific runbooks covering deployment, configuration, and troubleshooting procedures.
• Work closely with internal team members to progressively transfer MLOps, security, compliance, and data quality capability. 

Requirements

• Degree in Computer Science, Engineering, Information Security, or related field with 8+ years of experience in MLOps, security engineering, or compliance for cloud platforms.
• Proven experience operationalising and deploying ML models in production environments, including model monitoring and performance management.
• Hands-on experience with government or public-sector security compliance frameworks, with cloud security experience on AWS preferred.
• Experience implementing data privacy controls such as PII redaction and anonymisation.
• Experience securing large-scale, high-sensitivity government or public-sector platforms at national or large and complex scale, having served as the lead security or MLOps engineer for the majority of the project's duration.
• Extensive hands-on experience operating AWS cloud infrastructure, with familiarity with security and compliance requirements for cloud infrastructure.
• Experience deploying and operating data platforms (e.g. Databricks or comparable platforms) in cloud or government-compliant environments, from initial build through to production monitoring and incident response.
• Strong understanding of data quality dimensions: completeness, accuracy, format compliance, schema conformance, and latency, including building and validating these checks as automated tests within the pipeline.
• Demonstrated experience delivering comparable data platform projects internationally, at city-level or above, beyond the Singapore market.