Lead DevOps Engineer

Company Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

Job Description

We are seeking a highly skilled Lead 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

Qualifications

• 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.

Additional Information

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