M05 - DevSecOps Engineer

FPT Asia Pacific Pte Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

FPT Asia Pacific Pte Ltd in Singapore is seeking a DevSecOps Engineer to operationalise ML models, manage ML infra, and own platform security and data privacy across GCC environment.

You will collaborate with data scientists, ensure compliance with PDPA and GCC, implement automated data quality checks, and drive deployment readiness for cloud-based data platforms such as Databricks on AWS.

Qualifications

• 8+ years of experience in MLOps, security engineering, or cloud compliance.
• Proven track record deploying ML models to production with monitoring.
• Hands-on with government/public-sector security compliance, AWS preferred.
• Experience with data privacy controls such as PII redaction and anonymisation.
• Experience securing large-scale government/public-sector platforms.
• Extensive experience operating AWS cloud infrastructure and Databricks.

Responsibilities

• Own the operationalisation and deployment of ML models into production, coordinating handoff with Data Scientists.
• Manage ML infrastructure and monitor model performance in production for reliability and accuracy.
• Own platform security and GCC/GCC security baseline compliance across the data platform.

Skills

MLOps

Security engineering

Compliance

Data privacy controls

PII redaction

Anonymisation

Data quality checks

Infra security controls

Deployment automation

Monitoring production models

Education

Bachelor's degree in Computer Science / Engineering / Information Security

Tools

Databricks

AWS

Job description

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.