Devops Engineer with AI

About the role

A DevOps Engineer with AI is commonly called an AI DevOps Engineer, MLOps Engineer, or DevOps Engineer - AI/ML Platforms, depending on the responsibilities.

Responsibilities

• Build and automate the infrastructure, CI/CD pipelines, deployment, scaling, monitoring, and lifecycle management for AI/ML and Generative AI applications.
• Use DevOps tools such as CI/CD, Jenkins, GitHub Actions, GitLab CI, Azure DevOps.
• Work with cloud platforms like AWS, Azure, and GCP.
• Utilize containers like Docker, Kubernetes, and OpenShift.
• Implement Infrastructure as Code (IaC) using Terraform, Ansible, and CloudFormation.
• Apply MLOps practices including MLOps, MLflow, Kubeflow, SageMaker, and Azure ML.
• Deploy and manage Generative AI models using LLMs, Generative AI, model deployment, and inference.
• Monitor AI/ML applications using tools like Prometheus, Grafana, ELK, and Datadog.
• Perform AI operations tasks such as model monitoring, automated retraining, model versioning, and managing GPU infrastructure.

Requirements

N/A

Qualifications

N/A

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
• Miscellaneous: Monitoring (Prometheus, Grafana, ELK, Datadog), AI Operations (model monitoring, automated retraining, model versioning, GPU infrastructure)

Benefits

N/A

Pay

N/A

Schedule

N/A