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Salary
≈ $13k – $35k per year (Estimated)
Location
In office (Ho Chi Minh City)
Seniority
Senior · 3+ years exp

Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Oct 13, 2026.

Overview
Company
Impact
Profile match
NashTech is a technology services company with large delivery centres in Vietnam. It provides software engineering, data and cloud services to global clients. The company is part of the Nash Squared group.

Job description

  • Infrastructure & Automation
    • Implement Infrastructure as Code using Terraform, CloudFormation, Crossplane or ARM templates
    • Build and maintain CI/CD pipelines using Jenkins, GitLab CI, GitHub Actions, ArgoCD, or Azure DevOps
    • Manage containerized applications with Docker and Kubernetes orchestration
    • Automate deployment processes and configuration management
    • Optimize cloud infrastructure for performance, cost, and security
    • Strong scripting abilities (Python, Bash, PowerShell)
    • Nice to have: Experience using AI-assisted DevOps tools to detect risks, predict build failures, and review generated scripts, pipelines, and IaC configurations.

  • Technical Implementation
    • Configure monitoring and alerting systems (Prometheus, Grafana, ELK Stack)
    • Implement security best practices including vulnerability scanning and compliance checks
    • Manage version control workflows and branching strategies
    • Troubleshoot production issues and perform root cause analysis
    • Maintain backup and disaster recovery procedures

  • Collaboration & Mentoring
    • Work closely with development teams to improve deployment practices
    • Mentor junior DevOps engineers and provide technical guidance
    • Participate in on-call rotations and incident response
    • Document processes and procedures for knowledge sharing
    • Contribute to DevOps strategy and tool selection decisions

  • Problem-Solving Focus
    • Hands-on implementation of technical solutions
    • Proactive optimization of existing systems
    • Rapid troubleshooting and issue resolution
    • Continuous improvement mindset
    • Quality assurance and testing integration

  • Leadership Qualities
    • Technical mentorship of junior team members
    • Cross-team collaboration and communication
    • Knowledge sharing (especially in AI) and documentation

Qualifications

  • Must Have
    • At least 3 years of professional experience in DevOps, cloud engineering, infrastructure automation, platform engineering, or a related role.
    • Strong hands-on experience with Microsoft Azure, including deploying, operating, and optimizing cloud infrastructure.
    • Strong experience building and maintaining CI/CD pipelines using Azure DevOps.
    • Strong experience implementing Infrastructure as Code using Terraform.
    • Hands-on experience with Docker, Kubernetes, scripting, and automation using PowerShell, Bash, Python, or similar languages.
    • Experience with monitoring, logging, cloud security, secrets management, backup, and disaster recovery practices.
    • Strong understanding of Git workflows, branching strategies, artifact management, and secure deployment processes.
    • Proven ability to troubleshoot production issues, perform root cause analysis, and drive effective incident resolution.
    • Ability to work independently, manage priorities, and take end-to-end ownership of technical deliverables.
    • Good English communication and collaboration skills, with the ability to work effectively with international and cross-functional teams.

  • Preferred Qualifications
    • Experience with Azure-native services, particularly Azure Kubernetes Service (AKS), Azure Monitor, and Application Insights; relevant Azure, Kubernetes, or Terraform certifications are an advantage.
    • Experience working with international teams and providing technical guidance or mentorship to other engineers.
    • Familiarity with AI-assisted DevOps tools for pipeline troubleshooting, script generation, risk detection, and Infrastructure as Code reviews, with the ability to validate AI-generated outputs before implementation.

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