MLOps Engineer: Build and Scale End-to-End ML Pipelines

Quantum Technologies. LLC; Chicago (IL); On-site; USD 120,000 - 180,000; Full time

Job summary

Quantum Technologies LLC is seeking an experienced MLOps/DevOps engineer to design, build, and operate end-to-end ML pipelines. You will manage deployment, monitoring, and scaling of models across cloud platforms, while enabling automated testing, versioning, and retraining workflows.

The role requires collaboration with data scientists and ML engineers to optimize workflows, implement IaC, and ensure secure, cost-efficient production environments in a fast-paced setting.

Qualifications

• Hands-on experience with end-to-end MLOps pipelines.
• Experience deploying ML models and monitoring in production.
• Proficiency with Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).

Responsibilities

• Design, develop, and maintain MLOps pipelines from training to monitoring.
• Build and manage CI/CD pipelines for model testing, validation, deployment, and retraining.
• Deploy ML models to production across cloud environments and ensure reliability.
• Implement versioning, experiment tracking, and artifact management.
• Collaborate with data scientists to optimize deployment workflows.
• Manage cloud infrastructure across AWS, Azure, or GCP and ensure cost efficiency.
• Troubleshoot production issues and improve ML deployment processes.

Skills

MLOps frameworks

ML lifecycle management

Python scripting

CI/CD practices

Docker

Kubernetes

Cloud platforms

Monitoring & observability

Tools

Terraform

Ansible

GitHub Actions

Jenkins

Job description

Quantum Technologies LLC is seeking an experienced MLOps/DevOps engineer to design, build, and operate end-to-end ML pipelines. You will manage deployment, monitoring, and scaling of models across cloud platforms, while enabling automated testing, versioning, and retraining workflows.

The role requires collaboration with data scientists and ML engineers to optimize workflows, implement IaC, and ensure secure, cost-efficient production environments in a fast-paced setting.