MLOps Engineer

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

About the Role

We’re looking for an MLOps Engineer to build and maintain the infrastructure and automation required to deploy, monitor, and scale machine-learning models in production.

Responsibilities

• Build and maintain ML deployment pipelines and infrastructure.
• Automate model training, testing, deployment, and monitoring.
• Develop CI/CD pipelines for machine-learning 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.

Qualifications

• 3–7+ years of experience in MLOps, DevOps, ML engineering, or related roles.

Required Skills

• Experience with AWS, Azure, or GCP.
• Strong knowledge of Docker, Kubernetes, and CI/CD.
• Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience building production ML pipelines.
• Knowledge of model monitoring and observability.
• Familiarity with Git, Linux, APIs, and infrastructure automation.
• Strong troubleshooting and problem-solving skills.

Preferred Skills

• Experience with MLflow, Kubeflow, Airflow, or SageMaker.
• Experience with LLMs, RAG, or Generative AI.
• Terraform or Infrastructure-as-Code experience.
• Experience with feature stores and model registries.
• Experience optimizing large-scale ML workloads.

Pay range and compensation package

$125,000–$190,000+ annually, depending on experience, location, and technical expertise.

Equal Opportunity Statement

We are committed to diversity and inclusivity.