MLOps Engineer: Deploy, Monitor & Scale ML Systems

Elevexa Career LLC; United States; On-site; USD 125,000 - 190,000; Full time

Job summary

Elevexa Career LLC is seeking an MLOps Engineer to build and maintain production ML infrastructure, deploying, monitoring, and scaling models across AWS, Azure, or GCP.

You will collaborate with data scientists and ML engineers to productionize models, implement CI/CD for ML workflows, and manage Docker and Kubernetes workloads while improving scalability and cost efficiency. This role requires 3–7+ years of hands-on MLOps or DevOps experience and a strong problem-solving mindset.

Qualifications

• 3–7+ years of experience in MLOps, DevOps or related roles.
• Experience building production ML pipelines.
• Strong troubleshooting and problem-solving skills.
• Familiarity with Git, Linux, APIs, and infrastructure automation.
• Experience with AWS, Azure, or GCP.

Responsibilities

• Build and maintain ML deployment pipelines and infrastructure.
• Automate model training, testing, deployment, and monitoring.
• Develop CI/CD pipelines for ML workflows.
• Deploy and manage ML workloads across AWS, Azure, or GCP.
• Work with data scientists and ML engineers to productionize models.
• Monitor model performance, infrastructure health, and system reliability.
• Manage containerized ML workloads using Docker and Kubernetes.
• Implement model versioning, experiment tracking, and reproducible workflows.
• Improve scalability, reliability, and cost efficiency of ML infrastructure.
• Troubleshoot production ML systems and deployment issues.

Skills

AWS/Azure/GCP

Docker & Kubernetes

CI/CD

PyTorch/TF/Scikit-learn

Model monitoring

Git/Linux/APIs

Troubleshooting

Tools

CI/CD

Job description

Elevexa Career LLC is seeking an MLOps Engineer to build and maintain production ML infrastructure, deploying, monitoring, and scaling models across AWS, Azure, or GCP.

You will collaborate with data scientists and ML engineers to productionize models, implement CI/CD for ML workflows, and manage Docker and Kubernetes workloads while improving scalability and cost efficiency. This role requires 3–7+ years of hands-on MLOps or DevOps experience and a strong problem-solving mindset.