Devops Engineer with AI

ASM Tech Solutions; Chicago (IL); Hybrid; USD 130,000 - 180,000; Full time

Job summary

Cognizant in Chicago, IL is seeking an AI DevOps Engineer to design and implement scalable CI/CD pipelines for AI/ML and Generative AI applications in a hybrid work setting.

You will collaborate across data science, software, and IT operations to automate infrastructure, monitor models, and manage deployments in cloud environments (AWS, Azure, GCP).

The role requires strong scripting, containerization, and MLOps tooling expertise.

Qualifications

• Experience building CI/CD pipelines and deploying AI/ML applications.
• Strong knowledge of cloud platforms and container orchestration.
• Proficiency in Python and scripting.

Responsibilities

• Build and automate the infrastructure, CI/CD pipelines, deployment, scaling, monitoring, and lifecycle management for AI/ML and Generative AI applications.
• Collaborate with data scientists and engineers to operationalize AI models and pipelines.

Skills

CI/CD

Jenkins

GitHub Actions

GitLab CI

Azure DevOps

AWS

Azure

GCP

Docker

Kubernetes

OpenShift

Terraform

Ansible

CloudFormation

Python

Bash

PowerShell

MLOps

SageMaker

Kubeflow

Prometheus

Grafana

Datadog

Tools

Terraform

Ansible

CloudFormation

MLflow

Kubeflow

SageMaker

Azure ML

Prometheus

Grafana

ELK

Datadog

Docker

Kubernetes

OpenShift

Jenkins

GitHub Actions

GitLab CI

Azure DevOps

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.

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.