Full Time DevOps-DevSecOps Engineer Jobs, Salary up to SGD 7,500 in EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD. Central Region (Singapore) - Maukerja

Key Responsibilities

Cloud Infrastructure & Automation

• Design, deploy, and maintain cloud environments on AWS or GCP.
• Develop and manage infrastructure using Infrastructure-as-Code tools such as Terraform or similar technologies.
• Implement secure networking architectures, including VPCs, private connectivity, VPNs, peering, and network segmentation.
• Establish scalable multi-environment and multi-account cloud strategies.

Container Platform & Kubernetes

• Build and manage Kubernetes environments for production workloads.
• Configure cluster security, RBAC, autoscaling, workload isolation, and governance controls.
• Create standardized deployment frameworks for microservices, backend services, and AI-related workloads.
• Support both real-time and batch processing environments.

CI/CD & Release Automation

• Design and optimize CI/CD pipelines to streamline software delivery.
• Implement automated testing, deployment validation, and release management processes.
• Support deployment strategies such as blue-green, canary, and phased rollouts.
• Improve developer productivity through automation and deployment standardization.

Observability & Site Reliability

• Implement monitoring, logging, tracing, and alerting solutions.
• Define service reliability metrics, performance objectives, and operational standards.
• Support incident response processes, root-cause analysis, and continuous service improvement.
• Develop operational runbooks and support on-call readiness.

Security & DevSecOps

• Strengthen cloud security through identity and access management best practices.
• Manage secrets, certificates, and sensitive configuration securely.
• Implement vulnerability scanning, dependency analysis, and container security controls.
• Enforce security policies through automation and governance frameworks.
• Support audit readiness and compliance requirements.

AI/ML Platform Support

• Collaborate with data science and machine learning teams to operationalize AI solutions.
• Support model deployment, inference services, batch processing, and ML infrastructure.
• Monitor performance, availability, and resource consumption of AI workloads.
• Contribute to platform capabilities that improve AI product scalability.

Cost Optimization

• Monitor cloud spending and resource utilization.
• Implement tagging, budgeting, and optimization initiatives.
• Drive efficiency improvements without compromising performance or reliability.

Developer Experience

• Build self-service infrastructure capabilities and reusable engineering templates.
• Improve internal tooling and automation workflows.
• Maintain clear technical documentation and operational guidelines.
• Help create a smooth developer experience across engineering teams.

Requirements

Essential Skills & Experience

• Minimum 4 years of experience in DevOps, Site Reliability Engineering, Platform Engineering, or related infrastructure roles.
• Strong hands-on experience with AWS or Google Cloud Platform.
• Proven expertise with Infrastructure-as-Code tools, preferably Terraform.
• Solid Kubernetes administration and containerization experience (Docker, Helm, Kustomize, etc.).
• Strong knowledge of CI/CD pipelines and modern software delivery practices.
• Good understanding of Linux systems, networking, and automation scripting using Python and/or Bash.
• Experience implementing monitoring, logging, and observability solutions.
• Strong understanding of cloud security principles, IAM, secrets management, and infrastructure hardening.
• Ability to collaborate effectively with software engineers, product stakeholders, and technical leadership.
• Strong documentation and communication skills.

Preferred Qualifications

• Experience with GitOps practices and tools such as ArgoCD or Flux.
• Exposure to policy-as-code and infrastructure testing frameworks.
• Familiarity with data engineering or machine learning ecosystems.
• Experience working with distributed systems, streaming platforms, or large-scale data processing technologies.
• Knowledge of enterprise compliance, governance, and security review processes.
• Exposure to reliability engineering practices such as resilience testing or disaster recovery exercises.