MLOps Engineer

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

• 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.

Required Technical Skills:

• 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.

2. DevOps & CI/CD

• Strong experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
• Hands-on experience with Docker and Kubernetes.
• Infrastructure as Code (IaC) using Terraform, CloudFormation, or Ansible.
• Experience with Git, automation scripting, and deployment pipelines.
• Strong experience with at least one major cloud platform:
• AWS (SageMaker, EKS, S3, EC2, Lambda)

4. Monitoring & Observability

• Experience with Prometheus, Grafana, ELK Stack, or similar tools.
• Monitoring model performance, latency, availability, and resource utilization.
• Understanding of logging, alerting, and production troubleshooting.

5. Programming & Scripting

• Strong Python scripting skills.
• Familiarity with Bash or Shell scripting.
• Working knowledge of APIs, REST services, and microservices architecture.

Preferred Qualifications:

• Experience building scalable ML infrastructure and distributed training environments.
• Familiarity with feature stores and data orchestration tools such as Airflow.
• Experience with GPU-based workloads and ML model optimization.
• Understanding of LLMOps, GenAI deployment, and serving frameworks.
• Knowledge of security, governance, and compliance practices for ML systems.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.