Senior DevOps (Azure+On premises)

Description Project Description The primary goal of this initiative is to design and deploy a platform-agnostic ML system with NVIDIA technologies. An MLOps platform is a comprehensive set of tools and services designed to streamline the entire machine learning (ML) lifecycle—from data preparation and model development to deployment, monitoring, and governance. It blends DevOps principles with the unique needs of ML workflows, enabling teams to build, test, deploy, and maintain models more efficiently. An open-source tool will be used to train the model, like YOLO11 The open-source MLOps can be prepared like Kubeflow with MLflow and Apache Airflow. Nvidia TAO can also be used as a container for model Training along with Open-source MLOPS The solution can work on Premises and Cloud due to a containerized approach with Kubernetes

Requirements Qualifications 3+ years of hands-on experience with Azure Cloud 2+ years of hands-on experience with on-premises env Configuration of GitHub/branching strategy CI/CD (e.g. Azure Azure-based; Jenkins) Exp with security tools usage: SonarQube, DAST Strong understanding of prompt engineering techniques Excellent communication and presentation skills Passionate about learning and experimenting with new technologies

Job responsibilities Description As a DevOps, you will operate, update, and maintain the project’s Azure based on an on-premises infrastructure with a security-based domain.