AI DevOps Engineer

As an AI DevOps Engineer at Verdantas, you will be responsible for designing and managing scalable, secure DevOps infrastructure and CI/CD pipelines to facilitate efficient application deployment, automation, monitoring, and operations across various cloud environments. Key Responsibilities: - Designing and maintaining CI/CD pipelines for AI/ML model training, testing, and deployment. - Automating infrastructure provisioning using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation. - Managing containerized environments with Docker and Kubernetes, including GPU orchestration. - Collaborating with data scientists to operationalize ML models following MLOps best practices. - Monitoring model performance and system health utilizing tools like Prometheus, Grafana, or ELK Stack. - Implementing model versioning, experiment tracking, and reproducibility with tools like MLflow, DVC, or Kubeflow. - Ensuring security, compliance, and scalability of AI workloads on cloud platforms like AWS, Azure, and GCP. - Optimizing compute resource usage and cost for training and inference workloads. - Demonstrating proficiency in scripting languages (Python, Bash) and automation tools. - Utilizing ML platforms and tools such as MLflow, Kubeflow, SageMaker, and Vertex AI. - Having robust knowledge of containerization (Docker), orchestration (Kubernetes), and CI/CD tools like Jenkins, GitHub Actions, and GitLab CI. - Understanding model monitoring, drift detection, and retraining strategies. - Knowledge of data governance, security, and compliance in AI systems. - Holding certifications in cloud platforms or MLOps tools. Location and Work Set-up: - Pune, Maharashtra, India - Work Mode: In Office As an AI DevOps Engineer at Verdantas, you will be responsible for designing and managing scalable, secure DevOps infrastructure and CI/CD pipelines to facilitate efficient application deployment, automation, monitoring, and operations across various cloud environments. Key Responsibilities: - Designing and maintaining CI/CD pipelines for AI/ML model training, testing, and deployment. - Automating infrastructure provisioning using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation. - Managing containerized environments with Docker and Kubernetes, including GPU orchestration. - Collaborating with data scientists to operationalize ML models following MLOps best practices. - Monitoring model performance and system health utilizing tools like Prometheus, Grafana, or ELK Stack. - Implementing model versioning, experiment tracking, and reproducibility with tools like MLflow, DVC, or Kubeflow. - Ensuring security, compliance, and scalability of AI workloads on cloud platforms like AWS, Azure, and GCP. - Optimizing compute resource usage and cost for training and inference workloads. - Demonstrating proficiency in scripting languages (Python, Bash) and automation tools. - Utilizing ML platforms and tools such as MLflow, Kubeflow, SageMaker, and Vertex AI. - Having robust knowledge of containerization (Docker), orchestration (Kubernetes), and CI/CD tools like Jenkins, GitHub Actions, and GitLab CI. - Understanding model monitoring, drift detection, and retraining strategies. - Knowledge of data governance, security, and compliance in AI systems. - Holding certifications in cloud platforms or MLOps tools. Location and Work Set-up: - Pune, Maharashtra, India - Work Mode: In Office