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# DevOps Engineer

- **Company:** ASM
- **Location:** Chicago, IL, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Bash Shell, Cloud Computing, Continuous Delivery, Continuous Integration, DevOps, Github, Monitoring of Systems, Python (Programming Language), OpenShift, Windows PowerShell, Ansible, Prometheus, Azure Machine Learning, Datadog, Scripting, Graphics Processing Unit (GPU), Google Cloud, Large Language Models, Grafana, Generative AI, Cloudformation, Gitlab-ci, Kubernetes, Machine Learning Operations, Hardware Infrastructure, Terraform, Software Version Control, Docker, Jenkins
- **Published:** August 29, 2026
- **Apply:** https://www.careerbuilder.com/job-details/devops-engineer-with-ai-chicago-il--0e31d9b1-aa15-4afe-967b-3aad8d7e7bdc

## About the Role

Amazon Web Services (AWS), Ansible, Artificial Intelligence (AI), Bash Scripting, Biology, Biotech and Pharmaceutical, Cloud Computing, Continuous Deployment/Delivery, Continuous Integration, DevOps, Docker, GCP (Good Clinical Practices), GPU (Graphics Processing Unit), GitHub, Jenkins, Microsoft Windows Azure, Python Programming/Scripting Language, Windows PowerShell

## Description

A DevOps Engineer with AI is commonly called an AI DevOps Engineer, MLOps Engineer, or DevOps Engineer - AI/ML Platforms, depending on the responsibilities.
Chicago IL
Client: Cognizant
Hybrid
Life Science or Pharma domain
Key Skills

 * DevOps: CI/CD, Jenkins, GitHub Actions, GitLab CI, Azure DevOps
 * Cloud: AWS, Azure, GCP
 * Containers: Docker, Kubernetes, OpenShift
 * IaC: Terraform, Ansible, CloudFormation
 * AI/ML: MLOps, MLflow, Kubeflow, SageMaker, Azure ML
 * AI/GenAI: LLMs, Generative AI, model deployment, inference
 * Programming: Python, Bash, PowerShell
 * Monitoring: Prometheus, Grafana, ELK, Datadog
 * AI Operations: Model monitoring, automated retraining, model versioning, GPU infrastructure

Typical role: Build and automate the infrastructure, CI/CD pipelines, deployment, scaling, monitoring, and lifecycle management for AI/ML and Generative AI applications.
A DevOps Engineer with AI is commonly called an AI DevOps Engineer, MLOps Engineer, or DevOps Engineer - AI/ML Platforms, depending on the responsibilities.
Key Skills

 * DevOps: CI/CD, Jenkins, GitHub Actions, GitLab CI, Azure DevOps
 * Cloud: AWS, Azure, GCP
 * Containers: Docker, Kubernetes, OpenShift
 * IaC: Terraform, Ansible, CloudFormation
 * AI/ML: MLOps, MLflow, Kubeflow, SageMaker, Azure ML
 * AI/GenAI: LLMs, Generative AI, model deployment, inference
 * Programming: Python, Bash, PowerShell
 * Monitoring: Prometheus, Grafana, ELK, Datadog
 * AI Operations: Model monitoring, automated retraining, model versioning, GPU infrastructure

Typical role: Build and automate the infrastructure, CI/CD pipelines, deployment, scaling, monitoring, and lifecycle management for AI/ML and Generative AI applications.


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