Mid-Level Cloud & AI Engineer

About the role

Key Responsibilities

• Provision, configure, and support Google Cloud environments for AI, data, and enterprise application workloads.
• Support Vertex AI workspaces, model and data pipelines, batch or online inference services, and related deployment components.
• Implement infrastructure as code, configuration management, and environment standards using Terraform or comparable tools.
• Build and maintain CI/CD pipelines using Cloud Build, GitHub Actions, GitLab CI/CD, Jenkins, or equivalent tooling.
• Support services such as Cloud Run, GKE, BigQuery, Cloud Storage, Pub/Sub, Artifact Registry, IAM, Secret Manager, and Cloud Monitoring where appropriate.
• Package and deploy services with Docker and Kubernetes; maintain reliable release, rollback, and promotion processes.
• Automate operational tasks and data movement using Python, Shell, SQL, or comparable scripting and workflow tools.
• Implement monitoring, logging, alerting, dashboards, and basic cost controls for cloud and AI workloads.
• Troubleshoot infrastructure, application, pipeline, access, performance, and deployment issues; document root causes and preventive actions.
• Collaborate with engineers and customers across time zones, follow security and change‑control practices, and keep technical documentation current.

Essential Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related discipline, or equivalent professional experience.
• 3–5 years of experience in cloud engineering, DevOps, infrastructure, platform engineering, data engineering, or a related role.
• Hands‑on experience with Google Cloud Platform, including core compute, storage, networking, IAM, monitoring, and deployment services.
• Working knowledge of Vertex AI or other managed AI/ML platforms and the operational needs of data or model pipelines.
• Practical experience with Linux, Docker, Kubernetes, CI/CD, source control, and infrastructure‑as‑code concepts.
• Scripting capability in Python, Shell Script, or a similar language, plus basic SQL for data and pipeline troubleshooting.
• Understanding of cloud‑native architecture, system reliability, access control, secrets management, and operational support.
• Professional English communication skills and the ability to work effectively with international engineering teams.

Preferred Qualifications

• Google Associate Cloud Engineer, Professional Cloud DevOps Engineer, or equivalent certification.
• Experience with Vertex AI Pipelines, Dataflow, Airflow, MLflow, Terraform modules, or platform observability at scale.
• Experience with Google Agent Development Kit, Gemini, LLM application deployment, or MLOps/LLMOps.
• Experience supporting customer‑facing or offshore delivery projects and structured incident management.
• English language proficiency or experience supporting APAC‑based stakeholders.

Success Measures

• Development and production environments are provisioned consistently, securely, and with clear ownership.
• Deployment pipelines, monitoring, and operational documentation support predictable releases and rapid incident response.
• Cloud and AI pipeline issues are resolved within agreed priorities and recurring problems are reduced through automation.
• Platform cost, reliability, security, and performance are actively monitored and improved.

Benefits and Working Conditions

• Competitive compensation package, with 100% salary during probation, subject to company policy and the employment offer.
• Full social insurance contribution based on the applicable salary basis.
• Annual performance bonus and salary review in line with company policy.
• Premium healthcare insurance for employees and eligible family members from probation, subject to policy.
• Annual health check‑up and access to company‑sponsored training, professional courses, and certification examinations.
• Paid leave and public holidays in accordance with Vietnamese regulations and NTT DATA Vietnam policy.
• Hybrid working model, flexible start time with manager approval, and a global environment with international collaboration.
• Clear career development opportunities in cloud engineering, AI delivery, solution architecture, and technical leadership.

Work Schedule

• Monday to Friday, 08:00–12:00 and 13:00–17:00.
• Flexible start time between 07:45 and 09:00 with manager approval.