DevOps Engineer Supporting to Data Science Team

What You'll Do

Architect, deploy, and operate scalable data infrastructure in Azure Cloud.
Strong experience in Azure Cloud with observability stack.
Strong hands-on experience in containerized workloads using Docker and Kubernetes (AKS).
Strong hands-on experience in Airflow and Kubeflow.
Design and maintain CI/CD pipelines using Jenkins, Github Actions, and Terraform.
Harden containers and clusters (image scanning, RBAC, secrets, certificates, config management).
Hands-on experience in Snowflake.
Own platform operations: upgrades, capacity planning, incident response, and performance tuning.

Required Qualifications

6 years with Kubernetes (AKS).
6 years hands-on with Azure Cloud.
6; years deploying Docker images in Airflow and Kubeflow.
Strong understanding of Kubernetes architecture.
Good experience in Agentic AI framework.
6; years of Python for automation or data workflows.
Experience with Cloud Composer (Airflow).
Proficiency in Linux and scripting (Bash, Python).

Preferred Qualifications

Experience with Helm, Terraform, Jenkins and Github Actions.
Exposure to SQL/NoSQL and CloudSQL.
Hands-on migration of legacy apps to Kubernetes.
Familiarity with open-source data tooling and GCP services.
Background in cloud security & compliance.

Tech Stack

Azure ( Composer/Airflow, CloudSQL, Pub/Sub, Artifact Registry)

Docker
Kubernetes
Jenkins
Github
Terraform
Snowflake
Linux
Python
Airflow
Kubeflow

What To Expect In Interview

Be ready to discuss real-time issues and troubleshooting steps in AKS, Airflow and Kubeflow.

Prepare to walk through CI/CD pipelines you've built or maintained.

Brush up on Azure Services and observability practices.

Problem solving steps / Troubleshooting steps in Kubeflow and Airflow

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