DevOps / Cloud Engineer - DaAI

As a DevOps / Cloud Engineer, you will help build, automate, deploy, monitor, and operate the cloud infrastructure for our AI and data platform. You will work with senior platform architects, AI/ML engineers, data engineers, and product teams to support reliable deployments across cloud, hybrid, and enterprise customer environments.

This is a hands-on engineering role for someone who is comfortable working with cloud platforms, containers, CI/CD pipelines, infrastructure-as-code, observability, and secure deployment practices. The role is ideal for an engineer who wants to build production-grade platform capabilities for an enterprise AI product.

Responsibilities

Build and maintain cloud infrastructure components across AWS, Azure, GCP, or equivalent cloud platforms under the guidance of senior platform architects.

Implement infrastructure-as-code using Terraform, OpenTofu, cloud-native templates, or equivalent frameworks.

Build and maintain CI/CD pipelines for application services, AI agents, data pipelines, infrastructure changes, and environment promotions.

Support GitOps-based deployment workflows using tools such as Argo CD, Flux, GitHub Actions, GitLab CI/CD, Azure DevOps, or equivalent platforms.

Containerize application services, Python-based AI agents, data services, and supporting platform components using Docker.

Deploy and manage workloads on Kubernetes platforms such as EKS, AKS, GKE, enterprise Kubernetes distributions, or customer-managed clusters.

Create, update, and troubleshoot Helm charts, values files, environment-specific configurations, secrets injection, and rollback procedures.

Technical requirements

Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.

Practical experience with Terraform, OpenTofu, or equivalent infrastructure-as-code frameworks.

Experience with CI/CD tools such as GitHub Actions, GitLab CI/CD, Azure DevOps, Jenkins, or equivalent platforms.

Working knowledge of Docker, Kubernetes, Helm, and containerized workload deployment.

Familiarity with Kubernetes concepts such as deployments, services, ingress, config maps, secrets, namespaces, RBAC, and resource management.

Education

Bachelor of Engineering