MLOps Engineer: Build Scalable ML Pipelines

Machine Learning Technologies, LLC; Chicago (IL); On-site; USD 120,000 - 170,000; Full time

Job summary

Machine Learning Technologies, LLC in Chicago seeks an experienced MLOps Engineer to design, develop, and maintain end-to-end ML pipelines, deploy models, and monitor performance across cloud environments.

You will own CI/CD/CT pipelines, implement model versioning, and collaborate with data scientists to optimize deployment workflows while ensuring reliability, security, and cost efficiency.

Qualifications

• Hands-on experience with MLOps frameworks and tools.
• Experience managing the complete ML lifecycle.
• ML model deployment, monitoring, and versioning.
• Familiarity with MLflow, Kubeflow, or Metaflow.
• Understanding of Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps).
• Docker and Kubernetes proficiency; IaC with Terraform/Ansible/CloudFormation.
• Experience with cloud platforms (AWS, Azure, GCP).
• Monitoring with Prometheus, Grafana, ELK; observability.

Responsibilities

• Design, develop, and maintain end-to-end MLOps pipelines for model development, training, deployment, and monitoring.
• Build and manage CI/CD/CT pipelines for automated model testing, validation, deployment, and retraining.
• Deploy and manage machine learning models in production environments across cloud platforms.
• Implement model versioning, experiment tracking, model registries, and artifact management.
• Build and maintain containerized ML applications using Docker and Kubernetes.
• Automate infrastructure provisioning and configuration management using Terraform, Ansible, or similar tools.
• Collaborate with Data Scientists and ML Engineers to optimize model deployment workflows.
• Manage cloud infrastructure and services across AWS, Azure, or GCP.
• Ensure system reliability, scalability, security, and cost optimization for ML workloads.
• Troubleshoot production issues and continuously improve ML deployment and operational processes.

Skills

MLOps frameworks

ML lifecycle management

Model deployment & monitoring

MLflow / Kubeflow / Metaflow

Python

Bash / Shell scripting

APIs & microservices

Tools

Docker

Kubernetes

Terraform

Ansible

CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps)

Cloud platforms (AWS, Azure, GCP)

Prometheus, Grafana, ELK

Job description

Machine Learning Technologies, LLC in Chicago seeks an experienced MLOps Engineer to design, develop, and maintain end-to-end ML pipelines, deploy models, and monitor performance across cloud environments.

You will own CI/CD/CT pipelines, implement model versioning, and collaborate with data scientists to optimize deployment workflows while ensuring reliability, security, and cost efficiency.