MLOps Engineer - Scalable Production ML Pipelines

Tech Mirrors; Scottsdale (AZ); On-site; USD 120,000 - 170,000; Full time

Job summary

Tech Mirrors in Scottsdale, AZ is seeking an experienced MLOps Engineer to design, deploy, and manage scalable ML pipelines in production. The role emphasizes reliability, automation, governance, and seamless integration of models into enterprise systems.

You will build CI/CD pipelines for training, testing, and deployment; automate monitoring, retraining, and performance tuning; and work closely with Data Scientists and Data Engineers.

Qualifications

• Proficient in Python for ML model development and deployment.
• Experience building end-to-end ML pipelines and production ML systems.
• Hands-on experience with MLOps tools and cloud ML services.
• Familiar with CI/CD, monitoring, and governance for ML.
• Experience with AWS or other cloud platforms.
• Strong understanding of data pipelines and distributed systems.

Responsibilities

• Design end-to-end ML pipelines from data ingestion to deployment.
• Build and manage CI/CD pipelines for ML models.
• Automate model monitoring, retraining, and performance optimization.
• Collaborate with data scientists and data engineers to productionize models.
• Ensure scalability, reliability, and security of ML systems.
• Manage model versioning, experiment tracking, and lifecycle management.
• Apply governance, compliance and reproducibility best practices.

Skills

Python

TensorFlow

PyTorch

Scikit-learn

MLflow

Kubeflow

Airflow

SageMaker

Azure ML

Jenkins

GitHub Actions

GitLab CI

AWS

Data pipelines

ETL

Distributed systems

Tools

MLflow

Kubeflow

Airflow

SageMaker

Azure ML

Jenkins

GitHub Actions

GitLab CI

AWS

Job description

Tech Mirrors in Scottsdale, AZ is seeking an experienced MLOps Engineer to design, deploy, and manage scalable ML pipelines in production. The role emphasizes reliability, automation, governance, and seamless integration of models into enterprise systems.

You will build CI/CD pipelines for training, testing, and deployment; automate monitoring, retraining, and performance tuning; and work closely with Data Scientists and Data Engineers.