DevSecOps Engineer (IMDA)

VINOVA PTE. LTD.; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Vinova is seeking a DevSecOps Engineer to lead the deployment and operation of ML models and data platforms within government-compliant cloud environments.

You will collaborate with data scientists to ensure security, data quality, and regulatory compliance, and you will craft runbooks to enable scalable, reliable production pipelines. This role requires hands-on security, cloud architecture, and risk management across GCC or similar standards.

Qualifications

• Degree in Computer Science, Engineering, Information Security, or related field.
• 8+ years in MLOps, security engineering, or cloud compliance.
• Experience deploying ML models in production with monitoring and performance management.
• Hands-on with government or public-sector security frameworks.
• Experience implementing data privacy controls such as PII redaction and anonymisation.
• Experience securing large-scale government or public-sector platforms as lead security or MLOps engineer.

Responsibilities

• Deploy ML models into production and collaborate with Data Scientists.
• Manage ML infrastructure and monitor model performance for reliability.
• Ensure platform security and GCC baseline compliance and data privacy controls.
• Configure, deploy, and operate data platforms in GCC environments.
• Develop infrastructure runbooks for deployment, configuration, and troubleshooting.
• Collaborate with internal teams to transfer MLOps, security, and data quality capabilities.

Skills

MLOps

Security engineering

Cloud security

Data privacy

Compliance

AWS

Databricks

MLOps monitoring

Education

Bachelor's degree in CS/Engineering

Tools

AWS

Databricks

CI/CD pipelines

IAM

Job description

Company Overview

Vinova is an award-winning development company specializing in mobile, web, and enterprise applications. Since 2010, it has delivered global projects in IoT, blockchain, fintech, networking, and ecommerce, focusing on quality, flexibility, and speed.

Job Summary

The DevSecOps Engineer will lead the deployment and operational management of ML models and data platforms, ensuring security, compliance, and data quality in government-compliant cloud environments.

Responsibilities

• Deploy and operationalize ML models into production, collaborating closely with Data Scientists to ensure smooth handoff and integration


• Manage ML infrastructure and continuously monitor model performance to maintain reliability and accuracy over time


• Implement and maintain platform security and compliance with IM8, Government Commercial Cloud (GCC) security baselines, and relevant data regulations


• Apply data privacy controls such as blurring personally identifiable information (PII) in sensitive data sources like CCTV feeds


• Configure, deploy, and ensure operational readiness of data platforms within GCC environments


• Implement infrastructure security controls aligned with GCC security requirements


• Define and enforce data quality standards across the platform, ensuring data completeness, accuracy, schema conformity, and latency compliance before downstream use


• Develop detailed infrastructure runbooks covering deployment, configuration, and troubleshooting processes


• Collaborate with internal teams to transfer MLOps, security, compliance, and data quality capabilities progressively

Required competencies and certifications

• Degree in Computer Science, Engineering, Information Security, or related field


• Minimum 8 years’ experience in MLOps, security engineering, or compliance for cloud platforms


• Proven ability to operationalize and deploy ML models in production environments, including model monitoring and performance management


• Hands-on experience with government or public-sector security compliance frameworks


• Experience implementing data privacy controls such as PII redaction and anonymisation


• Experience securing large-scale, high-sensitivity government or public-sector platforms as lead security or MLOps engineer


• Extensive hands-on experience operating AWS cloud infrastructure with security and compliance expertise


• Experience deploying and operating data platforms (e.g., Databricks or comparable) in cloud or government-compliant environments from build to production monitoring and incident response


• Strong understanding of data quality dimensions including completeness, accuracy, format compliance, schema conformance, and latency, with experience building automated pipeline tests


• Demonstrated experience delivering comparable data platform projects internationally at city-level or above