Responsibilities:
Design and maintain CI/CD pipelines in Azure DevOps for both traditional applications and ML workloads.
Develop and manage MLOps workflows for model training, validation, deployment, and monitoring.
Automate deployment of services using ArgoCD, Helm, and GitOps best practices.
Manage and operate production-grade Kubernetes clusters (on-prem and Azure AKS).
Implement Infrastructure as Code (IaC) using Terraform.
Collaborate closely with data scientists, software engineers, and infrastructure teams to support end-to-end delivery.
Monitor system performance, availability, and reliability using tools like Prometheus, Grafana, and EFK stack.
Ensure best practices in security, version control, logging, and compliance.
Requirements:
3+ years of experience as a DevOps Engineer in a production environment.
Proven expertise in Azure DevOps (Pipelines, Repos, Artifacts).
Proficient in ArgoCD, Kubernetes, and container orchestration.
Experience with Docker, Git, and versioning strategies.
Scripting knowledge in Python, Bash, or PowerShell.
Solid understanding of CI/CD, GitOps, and cloud-native architecture.
Experience working in hybrid (on-prem + cloud) environments.
Customer-facing role; fluent English (spoken and written) is required.
Basic Network understanding is must.
Nice to Have:
Experience in OpenShift platforms.
Experience working in AWS.
Familiarity with monitoring/logging solutions: Prometheus, Grafana, Elasticsearch, Kibana.
Background working with data pipelines or real-time streaming systems.
Hands-on experience with MLOps platforms (e.g., MLflow, Kubeflow, Azure ML).
Azure Certifications (e.g., AZ-400, AZ-104).
Knowledge in different architecture environments.
