ND - Jr/Ssr DevOps Engineer (AI-Focused) - Job8185

Summary

We are looking for a Jr/SSr DevOps Engineer to support AI and engineering teams by building, automating, and maintaining scalable, secure, and reliable cloud infrastructure. This role plays a key part in enabling fast and safe deployment of AI/ML applications through well-designed CI/CD pipelines and infrastructure-as-code practices.

You will collaborate closely with AI, backend, and data engineers to streamline development workflows, improve deployment reliability, and ensure production readiness across environments.

Responsibilities

• Design, automate, and maintain scalable cloud infrastructure to support AI and backend systems.
• Build and maintain CI/CD pipelines for reliable and repeatable deployments.
• Implement Infrastructure as Code (IaC) to provision and manage environments consistently.
• Ensure system reliability, availability, observability, and security across environments.
• Monitor applications and infrastructure, proactively identifying and resolving issues.
• Collaborate with AI, data, and backend teams to support model deployment and operational workflows.
• Apply DevOps and cloud best practices related to security, networking, and access control.

Requirements

• 1-3 years of hands-on DevOps or site reliability experience supporting cloud-native applications.
• Practical experience with AWS (preferred), Azure, or GCP.
• Experience with Docker and Kubernetes for containerization and orchestration.
• Experience building CI/CD pipelines using Jenkins, GitHub Actions, or Azure DevOps.
• Good scripting skills in Python and/or Bash for automation and tooling.
• Familiarity with monitoring and observability tools such as CloudWatch, Prometheus, Grafana, or ELK.
• Experience with Security & Best Practices: IAM, secrets management, networking fundamentals.
• Good collaboration and communication skills; able to work cross-functionally with AI, data, and backend teams.

Nice to Have

• Experience with Terraform and CloudFormation.
• Familiarity with AI deployment patterns (model registries, feature stores, model serving).