Azure ML & Cloud Automation Engineer

V2 Solutions; Hinoba-an; On-site; PHP 1,800,000 - 2,400,000; Full time

Job summary

UST is seeking a Lead I - ML Engineering to drive end-to-end ML deployment automation across Azure, Databricks, and Kubernetes. You will build CI/CD pipelines, deploy Databricks Jobs and MLflow models, and run microservices on AKS/ARO. The role emphasizes security, governance, and cost optimization within MLOps.

You will collaborate with ML engineers, data engineers, and application teams to deliver scalable, reliable platforms and streamlined ML workflows across cloud environments.

Qualifications

• Experience with Azure, AKS, ARO Databricks.
• Deploy ML models with MLflow on Kubernetes.
• Proficient in Python and Bash/PowerShell.
• Strong cloud security, networking, and distributed systems knowledge.

Responsibilities

• Build CI/CD/CT pipelines for ML models using Azure DevOps / GitHub Actions / Jenkins.
• Develop deployment workflows for Databricks Jobs and MLflow models.
• Operate microservices on AKS / ARO and automate infrastructure with Terraform.
• Manage Databricks workspaces and AKS clusters; optimize networking and serving environments.
• Implement monitoring, logging, and reliability practices for ML systems.
• Collaborate with ML engineers, data engineers, and application teams; govern security and cost.

Skills

Azure

AKS

ARO Databricks

MLflow

Python

Bash/PowerShell

Cloud security

Networking

Distributed systems

Tools

Databricks

Terraform

Azure DevOps

GitHub Actions

Jenkins

Kubernetes

Docker

Job description

UST is seeking a Lead I - ML Engineering to drive end-to-end ML deployment automation across Azure, Databricks, and Kubernetes. You will build CI/CD pipelines, deploy Databricks Jobs and MLflow models, and run microservices on AKS/ARO. The role emphasizes security, governance, and cost optimization within MLOps.

You will collaborate with ML engineers, data engineers, and application teams to deliver scalable, reliable platforms and streamlined ML workflows across cloud environments.