Azure MLOps Architect: Production ML Pipelines

EPAM Systems Inc; Chile; Remote; CLP 18,000,000 - 28,000,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off and sick leave

Upskilling and certification courses

LinkedIn Learning access

Job summary

EPAM Systems Inc is seeking a Senior Azure MLOps Engineer to design and operate production ML pipelines on cloud platforms. The role focuses on building automated CI/CD workflows, model lifecycle management, and robust monitoring across Azure and Kubernetes environments.

You will collaborate with researchers and engineers, implement automated tests, and optimize deployment performance for scalable AI delivery while maintaining infrastructure as code.

Qualifications

• 3+ years deploying ML models to production
• Hands-on experience with Microsoft Azure for ML workloads
• Experience building MLOps pipelines with CI/CD and model lifecycle management
• Strong Python skills with PyTorch and/or TensorFlow

Responsibilities

• Design production-grade MLOps pipelines for training, deployment, and monitoring
• Build CI/CD workflows for model packaging, validation, and rollout
• Deploy ML and foundation models on Microsoft Azure and Kubernetes platforms
• Implement model lifecycle management across versioning and promotion stages
• Develop monitoring and alerting for model performance, drift, and service health
• Automate testing and quality gates for ML code and pipelines
• Collaborate with data scientists and researchers to operationalize experiments
• Coordinate cross-functional delivery with platform and engineering teams
• Maintain infrastructure as code to enable reproducible environments
• Troubleshoot production issues across pipelines, services, and runtime dependencies

Skills

Python

CI/CD

Collaboration

Testing

Automation

PyTorch

TensorFlow

Tools

Azure

Azure DevOps

AKS

Terraform

Databricks

GCP

Job description

EPAM Systems Inc is seeking a Senior Azure MLOps Engineer to design and operate production ML pipelines on cloud platforms. The role focuses on building automated CI/CD workflows, model lifecycle management, and robust monitoring across Azure and Kubernetes environments.

You will collaborate with researchers and engineers, implement automated tests, and optimize deployment performance for scalable AI delivery while maintaining infrastructure as code.