DevOps Engineer — Data & AI Platforms AIRLKLHV

Job Title: DevOps Engineer Data & AI Platforms

Location: Remote / Hybrid
Employment Type: Contract / Full-Time

Overview

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.

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

Required Qualifications

• 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

Preferred Qualifications

• 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

Must-Have Skills

• 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

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