DevOps Engineer — Data & AI Platforms - AIRLKLHV

NavitasPartners • Chicago, IL

11h • Remote

About The Position

We are seeking a skilled DevOps Engineer – Data & AI Platforms to support the automation, deployment, and reliability of modern data and AI environments. This role is ideal for someone who can bridge traditional DevOps practices with the unique demands of data engineering, machine learning, and AI-driven systems. The ideal candidate will play a key role in enabling scalable, secure, and production-ready platforms for analytics and AI workloads.

Requirements

• Proven experience in DevOps supporting cloud, data, or AI/ML platforms
• Hands-on experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps, etc.)
• Strong experience with infrastructure-as-code tools (Terraform, CloudFormation, ARM/Bicep, Ansible)
• Working knowledge of cloud platforms (AWS, Azure, or GCP)
• Experience with containerization and orchestration (Docker, Kubernetes, OpenShift)
• Strong scripting skills (Python, Bash, PowerShell, etc.)
• Understanding of monitoring, logging, and production support practices
• Experience building DevOps pipelines for data, analytics, or AI/ML environments
• Strong hands-on experience with cloud automation, CI/CD, containers, and infrastructure-as-code
• Ability to support production-grade systems with reliability and scalability

Nice To Haves

• Experience supporting data pipelines, analytics platforms, or ML workflows
• Familiarity with MLOps tools and AI deployment pipelines
• Experience in enterprise or regulated environments
• Knowledge of security best practices in cloud and DevOps environments

Responsibilities

• Build and maintain CI/CD pipelines for data platforms, analytics applications, ML models, and AI services
• Automate infrastructure provisioning and deployment workflows using infrastructure-as-code tools
• Support and manage cloud environments across AWS, Azure, GCP, or hybrid ecosystems
• Collaborate with data engineers, ML engineers, and platform teams to standardize deployment practices
• Implement monitoring, logging, alerting, and observability for pipelines and services
• Manage containerized workloads using Docker, Kubernetes, OpenShift, or similar platforms
• Enforce secure DevOps practices including secrets management, access control, and compliance checks
• Troubleshoot deployment issues, pipeline failures, and performance bottlenecks

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 1-10 employees