ML Devops Engineer

Role: ML Devops Engineer
Location: Chicago, IL (Need Local Onsite)
Exp: 8+ Years

We are looking for an ML DevOps Engineer to build and run the pipelines and infrastructure that take machine learning and AI models from development into production. You'll work with data scientists, engineers and platform teams to make sure models are deployed reliably, monitored well and kept secure as they scale.

Experience

• 8+ years overall, with 3+ years in MLOps or DevOps for machine learning and AI workloads

Required Skills

• Strong hands-on experience with at least one major cloud platform: AWS, Azure or Google Cloud Platform.

• Experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Azure ML or Databricks.

• Skilled with Docker and Kubernetes.

• Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI or Azure DevOps.

• Infrastructure as Code with Terraform, CloudFormation or similar tools.

• Good scripting skills in Python and Bash.

• Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog or CloudWatch.

• Good understanding of the ML lifecycle and how models are served in production.

Good to Have Skills

• Experience deploying and running Generative AI or LLM-based applications, including API gateways and model serving.

• Cloud certifications such as AWS DevOps or ML Specialty, or Azure DevOps Engineer.

• Exposure to finance or other regulated industries.

• Experience in large enterprise or client-facing consulting settings.

Roles & Responsibilities

• Design, build and maintain CI/CD pipelines for ML and AI models.

• Automate model training, testing, deployment and rollback.

• Set up and manage cloud infrastructure for model hosting and serving.

• Containerize models and services, and manage them on Kubernetes or similar platforms.

• Put monitoring in place for model performance, data drift, latency and cost.

• Manage model versioning, experiment tracking and the model registry.

• Work with data science teams to make models production-ready.

• Apply security, access control and compliance standards across ML environments.

• Troubleshoot production issues and support reliable, always-on operations.

• Document processes and promote best practices across teams.