Storable Careers - One Posting

DevOps II

Storable Careers - One Posting · Hyderabad, Telangana, India

Software Development · 501-1,000 employees

7 h ago
Mid (2-5 yrs) Full-time India
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About the role

Design and manage CI/CD pipelines and scalable infrastructure using Kubernetes and containerization tools. Support AI/ML model deployment and collaborate with development and QA teams for seamless releases.

What they look for

Kubernetes Python Bash Shell CI/CD Jenkins GitHub Actions GitLab CI AWS Azure GCP Docker Terraform CloudFormation MLOps Prometheus

Requirements

Requires over 4 years of DevOps experience with strong proficiency in Kubernetes, scripting languages, and cloud platforms. Knowledge of Infrastructure as Code and basic AI/ML workflows is highly desired.

Full description

Job Overview:

We are looking for a skilled DevOps Engineer with 4+ years of experience in building, automating, and managing scalable infrastructure. The ideal candidate should have strong expertise in Kubernetes, scripting, and a basic understanding of AI/ML workflows.

Key Responsibilities:

  • Design, implement, and manage CI/CD pipelines for application deployment
  • Work with containerization and orchestration tools, especially Kubernetes
  • Automate infrastructure provisioning and configuration management
  • Monitor system performance, reliability, and availability
  • Collaborate with development and QA teams for seamless releases
  • Troubleshoot and resolve infrastructure and deployment issues
  • Support AI/ML model deployment and basic MLOps workflows

Required Skills:

  • 4+ years of experience in DevOps or related roles
  • Strong hands-on experience with Kubernetes
  • Proficiency in scripting languages such as Python, Bash, or Shell
  • Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, etc.)
  • Knowledge of cloud platforms (AWS / Azure / GCP)
  • Experience with containerization tools like Docker
  • Understanding of Infrastructure as Code (Terraform, CloudFormation, etc.)

Preferred Skills:

  • Basic knowledge of AI/ML concepts and model deployment
  • Familiarity with MLOps tools and workflows
  • Experience with monitoring tools (Prometheus, Grafana, ELK stack)
  • Knowledge of security best practices in DevOps