Cmc-Apac Private Limited · MyCareersFuture · 1w
DevSecOps Engineer (GenAI)
Singapore, Central- Posted
- 2026-09-25 (1w)
- Place
- Singapore, Central
- Commitment
- Full Time
- Salary
- SGD 5,500 – 6,500 / month
- Experience
- 2+ YOE
- Department
- Engineering
- Source
- MyCareersFuture (the employer’s own listing)
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Skills in this posting
KubernetesTechnical SkillsAWSCloud NetworkingGitLabTechnical DocumentationPostgreSQLOpenSearchAmazon Web ServicesCI/CDGitLab CITerraform
We are seeking a DevSecOps Engineer to manage the security, reliability, scalability, and compliance of cloud platforms, Kubernetes environments, CI/CD pipelines, and AI infrastructure. This role will drive secure platform operations, infrastructure automation, and cloud governance while enabling the safe deployment of Generative AI workloads.
Key Responsibilities
Cloud & Kubernetes Platform
• Manage and scale AWS environments and Kubernetes clusters on Amazon EKS.
• Own cluster architecture, deployment, security, upgrades, autoscaling, monitoring, and incident resolution.
• Ensure platform reliability, resilience, and operational readiness.
AWS Infrastructure & Networking
• Administer AWS services including RDS PostgreSQL, OpenSearch, Bedrock, and ELB.
• Design and support secure AWS network architectures, covering VPCs, subnets, Transit Gateway, firewalls, WAF, PrivateLink, VPN, Direct Connect, DNS, and load balancing.
• Apply network segmentation and least-privilege principles.
Reliability, Monitoring & Operations
• Establish SLI/SLO frameworks, monitoring dashboards, alerting, logging, metrics, and tracing.
• Lead incident management, root cause analysis, capacity planning, backup validation, and disaster recovery testing.
Infrastructure Automation & CI/CD
• Build and manage AWS and Kubernetes environments using Terraform.
• Maintain GitLab CI/CD pipelines with:Automated testingSAST / DASTDependency and container scanningPolicy enforcementRelease approvalsRollback mechanismsAudit-ready deployment records
Security & Compliance
• Implement security controls throughout the platform lifecycle.
• Conduct threat modelling, architecture reviews, vulnerability management, patching, hardening, risk assessments, and audit support.
• Translate regulatory and security requirements into automated platform controls.
Identity & Supply Chain Security
• Manage IAM, workload identities, privileged access, secrets, certificates, container security, SBOMs, and dependency governance.
• Ensure secure handling of credentials and platform changes.
Platform Transition & Documentation
• Review existing platform architecture and operational configurations.
• Support infrastructure knowledge transfer and operational transition activities.
• Maintain architecture diagrams, runbooks, procedures, and technical documentation.
Requirements
Experience
• 2 to 5+ years of experience in DevOps, DevSecOps, Cloud Engineering, or related roles.
• Strong hands-on experience with Kubernetes and Terraform in production environments.
Technical Skills
• Strong AWS infrastructure and Amazon EKS expertise.
• Experience with Kubernetes networking, ingress controllers, load balancers, service discovery, network policies, and connectivity troubleshooting.
• Strong knowledge of AWS networking, including VPCs, Transit Gateway, PrivateLink, Route 53, VPN, Direct Connect, Security Groups, and Network ACLs.
• Experience designing and maintaining GitLab CI/CD pipelines.
Operational Excellence
• Experience with monitoring, alerting, incident response, root cause analysis, capacity management, backup and recovery, and disaster recovery planning.
Regulated Environments
• Experience supporting platforms within highly regulated environments.
• Understanding of governance, audit requirements, change management, risk management, segregation of duties, and secure data handling.
Communication
• Ability to communicate technical concepts to both technical and business stakeholders.
• Strong documentation skills, including architecture diagrams, operational procedures, runbooks, and risk assessments.
Preferred Skills
• Experience operating AI or agentic AI platforms.
• Familiarity with agent observability, distributed tracing, prompt management, evaluation pipelines, and secure model integrations.
• Experience supporting production AI/ML workloads, including monitoring latency, usage, cost, and responsible AI controls.
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