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Job DescriptionWe are seeking a highly skilled Lead Azure DevOps Engineer to join our team and drive the end-to-end deployment, scalability, and operationalization of machine learning models in production. You will collaborate closely with data scientists, data engineers, and DevOps teams to ensure seamless CI/CD, reproducibility, monitoring, and governance of ML pipelines
Key Responsibilities
- Design, implement, and maintain CI/CD pipelines for deploying and monitoring microservices efficiently.
- Manage infrastructure as code using Terraform for repeatable and scalable provisioning.
- Deploy and optimize containerized applications using Docker and Azure services (Container Apps, Container Registry, Key Vault, Service Bus, Blob Storage).
- Apply best practices for securing Docker images, including vulnerability scanning, reducing image size, and optimizing build efficiency.
- Implement and maintain Azure Monitor for logging, monitoring, and alerting to ensure system reliability.
- Ensure security best practices across cloud environments, including secrets management, access control, and compliance.
- (Nice to have) Design and manage multi-client architectures within shared pipelines and storage accounts in Azure Blob Storage
- 6+ years of experience in DevOps or MLOps with a strong focus on production-grade ML solutions.
- Strong expertise in Azure, particularly with CI/CD, container orchestration, and cloud security. Proficiency in Terraform for infrastructure automation.
- Deep understanding of Docker, including best practices for securing, optimizing, and managing images.
- Experience with Azure Monitor for centralized logging, monitoring, and alerting. Strong knowledge of microservices architecture and best practices for scalable deployments.
- Experience in security best practices, including secrets management and role-based access.
Preferred Qualifications
- Experience with Databricks, particularly for ML workflows and data engineering.
- Experience deploying, securing and managing vector databases Hands-on experience with MLFlow for model tracking and deployment.
- Best practices for multi-client architecture in shared pipelines and storage.
- Python experience for microservices development, if interested in contributing to application
- Docker Compose for local development and multi-container applications
