We are redirecting you to the source. If you are not redirected in 3 seconds, please click here.

Lead DevOps Engineer (Azure, Terraform) at NorthBay Solutions. Location Information: India. 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