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.
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.
