Senior DevSecOps Engineer

Role & Responsibilities

The Senior DevSecOps Engineer will be responsible for the operationalisation, security, compliance, and production readiness of the data and ML platform.

Operationalise and deploy ML models into production, working closely with Data Scientists during the transition from model development to deployment.

Manage ML infrastructure and monitor model performance, reliability, and accuracy in production.

Manage the configuration, deployment, and operational readiness of the data platform within the Government Commercial Cloud (GCC) environment.

Own platform security and compliance, ensuring alignment with IM8, GCC security baselines, PDPA, and other applicable data compliance requirements.

Implement infrastructure security and data privacy controls, including PII redaction, anonymisation, and blurring of sensitive information from sources such as CCTV feeds.

Establish and maintain data quality standards covering completeness, accuracy, format and schema compliance, and latency.

Build and validate automated data quality checks within data pipelines.

Develop infrastructure runbooks covering deployment, configuration, monitoring, troubleshooting, and incident response.

Work closely with internal teams to progressively transfer MLOps, security, compliance, and data quality capabilities.

Requirements

Degree in Computer Science, Engineering, Information Security, or a related field.

8+ years of relevant experience

in MLOps, DevSecOps, security engineering, cloud infrastructure, or compliance.

Proven experience deploying and operationalising ML models in production, including model monitoring and performance management.

Strong hands-on experience with

AWS cloud infrastructure , including cloud security and compliance requirements.

Experience deploying and operating data platforms such as

Databricks or comparable platforms , from initial implementation through production monitoring and incident response.

Hands-on experience with government or public-sector security and compliance frameworks, preferably within GCC environments.

Experience implementing data privacy controls, including

PII redaction and anonymisation .

Experience securing large-scale, high-sensitivity government or public-sector platforms, preferably serving as a lead Security, DevSecOps, or MLOps Engineer for a significant portion of the project lifecycle.

Demonstrated experience delivering comparable large-scale data platform projects internationally, at

city-level or above , outside the Singapore market.

Strong understanding of data quality dimensions, including completeness, accuracy, format compliance, schema conformance, and latency, with experience implementing automated validation within data pipelines.

Strong communication and stakeholder management skills, with the ability to transfer technical capabilities and knowledge to internal teams.

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