Industrialize and automate all delivery processes, including CI/CD, MLOps, and operational workflows for RAG and agentic AI workloads on Azure
Design, build, and maintain reliable CI/CD pipelines for both infrastructure and application development, including feature flags, blue‑green, and canary deployment strategies
Manage Azure Kubernetes Service environments, focusing on containerization best practices, scaling policies, and high‑availability setups
Implement end‑to‑end MLOps practices using Azure Machine Learning, covering model deployment, monitoring, and governance
Securely manage secrets, keys, and configuration using Key Vault and Managed Identities
Enable platform reliability, observability, and automation across the full AI application lifecycle

5+ years of experience in DevOps or MLOps engineering, ideally within AI‑intensive product environments
Strong hands‑on experience with Azure DevOps or GitHub Actions for CI/CD orchestration
Solid expertise in Infrastructure as Code (Terraform preferred)
Proven experience running and scaling workloads on Azure Kubernetes Service (AKS)
Familiarity with Azure Machine Learning for model deployment, monitoring, and lifecycle management
Strong understanding of secrets management, identity, and configuration management using Azure Key Vault and Managed Identities
Demonstrated ability to build robust, automated pipelines and deployment strategies that support reliable, scalable AI applications

Senior DevOps, MLOps Automation Engineer

Key skills