AI Platform DevOps Engineer — MLOps & Cloud

TOSS-EX PR PTE. LTD.

Singapore

On-site

SGD 120,000 - 180,000

Full time

3 days ago

Be an early applicant

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

TOSS-EX PR PTE. LTD. is seeking an experienced DevOps Engineer to design, build and operate scalable AI platforms across Azure and AWS. You will develop reusable Terraform modules, automate CI/CD pipelines, and enforce GitOps and DevSecOps practices.

You will implement scalable cloud infrastructure, manage AI platform environments, and ensure security, reliability and governance for AI/ML workloads. Strong IaC, containerization and cloud skills are essential.

Qualifications

Diploma or degree in IT/CS or related field.
3+ years of experience in Cloud Engineering, Platform Engineering, DevOps, or Infrastructure Automation.
Hands-on experience with Terraform and Infrastructure as Code.
Strong experience designing and managing CI/CD pipelines.
Experience with Docker and Kubernetes.
Proficiency in at least one cloud platform: Azure or AWS.
Experience with scripting languages such as Python, PowerShell, Bash, or Shell scripting.
Knowledge of Git, version control, and software delivery best practices.
Experience supporting AI/ML platforms and MLOps environments.
Knowledge of monitoring and observability tools.

Responsibilities

Design, implement, and maintain cloud infrastructure using Terraform and IaC principles.
Build and manage AI platform environments across Azure, AWS,
Develop reusable Terraform modules and infrastructure standards.
Implement infrastructure automation to improve scalability, reliability and operational efficiency.
Design and maintain CI/CD pipelines using Azure DevOps, GitHub Actions, GitLab CI, or Jenkins.
Automate build, test, deployment, and release processes for AI and application workloads.
Implement GitOps and DevSecOps best practices.
Support deployment and lifecycle management of AI/ML and Generative AI applications.
Build and maintain MLOps capabilities including model deployment, versioning, monitoring, and governance.
Enable integration with AI services such as Azure OpenAI, OpenAI APIs, Azure AI Services, AWS Bedrock, or Vertex AI.
Deploy and manage containerized workloads using Docker and Kubernetes.
Maintain scalable Kubernetes environments for AI and enterprise applications.
Troubleshoot performance, security, and reliability issues within container platforms.
Implement cloud security best practices, IAM, secrets management, and compliance controls.
Ensure platform reliability, observability, logging, and monitoring.
Collaborate with security and governance teams to enforce enterprise standards.