The Lead DevOps Engineer will design and manage CI/CD pipelines, develop Infrastructure-as-Code, scale container orchestration, and implement MLOps best practices alongside cloud infrastructure optimization.
Job Title: Lead DevOps Engineer (Azure, Terraform)
Employment Type: Full-time Remote (India)
About the Role:
NorthBay, a leading AWS Premier Partner, is seeking a highly skilled Lead DevOps (Azure, Terraform) to join its growing cloud and AI engineering team. This role is ideal for candidates with a strong foundation in cloud DevOps practices and a passion for implementing scalable MLOps solutions.
Key Responsibilities:
● Design, implement, and manage CI/CD pipelines using tools such as Jenkins, GitHub Actions, or Azure DevOps● Develop and maintain Infrastructure-as-Code using Terraform
● Manage and scale container orchestration environments using Kubernetes, including experience with larger production-grade clusters
● Ensure cloud infrastructure is optimized, secure, and monitored effectively
● Collaborate with data science teams to support ML model deployment and operationalization
● Implement MLOps best practices, including model versioning, deployment strategies (e.g., blue-green), monitoring (data drift, concept drift), and experiment tracking (e.g., MLflow)
● Build and maintain automated ML pipelines to streamline model lifecycle management
Required Skills:
● 8 to 12 years of experience in DevOps and/or MLOps roles● Proficient in CI/CD tools: Jenkins, GitHub Actions, Azure DevOps
● Strong expertise in Terraform, including managing and scaling infrastructure across large environments
● Hands-on experience with Kubernetes in larger clusters, including workload distribution, autoscaling, and cluster monitoring
● Strong understanding of containerization technologies (Docker) and microservices architecture
● Solid grasp of cloud networking, security best practices, and observability
● Scripting proficiency in Bash and Python
Preferred Skills:
● Experience with MLflow, TFX, Kubeflow, or SageMaker Pipelines● Knowledge of model performance monitoring and ML system reliability
● Familiarity with AWS MLOps stack or equivalent tools on Azure/GCP
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