DevOps Engineer

ASM

Chicago, IL, United States

6 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.careerbuilder.com

Tech stack

Artificial Intelligence Amazon Web Services Microsoft Azure Bash Shell Cloud Computing Continuous Delivery Continuous Integration DevOps Github Monitoring of Systems Python (Programming Language) OpenShift

+20 more

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

Job 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.

Requirements

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