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.