MLOps Engineer — Chicago, IL

Key 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.

• Implement model monitoring, performance tracking, data drift detection, and model drift detection.

• 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

1. MLOps & Machine Learning

• 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.

3. Cloud Platforms

• Strong experience with at least one major cloud platform:

• AWS (SageMaker, EKS, S3, EC2, Lambda)

• Microsoft Azure (Azure ML, AKS, Azure DevOps)

• Google Cloud Platform (Vertex AI, GKE, Cloud Storage)

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.

Ideal Candidate Profile

The ideal candidate should have a strong combination of DevOps + Cloud + Machine Learning deployment experience. We are particularly interested in professionals who have moved ML models from development environments into scalable, production-ready systems.

Keywords for Recruitment

MLOps, ML DevOps, Machine Learning Operations, ML Model Deployment, MLflow, Kubeflow, Kubernetes, Docker, Python, AWS SageMaker, Azure ML, Vertex AI, CI/CD, Terraform, Jenkins, Model Monitoring, Model Registry, Infrastructure as Code, LLMOps.