AI Ops Engineer

Job Description

Role & responsibilities Responsibilities: Design, implement, and manage AIops solutions to automate and optimize AI/ML workflows. Collaborate with data scientists, engineers, and other stakeholders to ensure seamless integration of AI/ML models into production. Monitor and maintain the health and performance of AI/ML systems. Develop and maintain CI/CD pipelines for AI/ML models. Implement best practices for model versioning, testing, and deployment. Troubleshoot and resolve issues related to AI/ML infrastructure and workflows. Stay up-to-date with the latest AIops, MLOps, and Kubernetes tools and technologies. Preferred candidate profile Requirements: Proven experience in AIops, MLOps, or related fields. Strong proficiency in Python and experience with FastAPI. Strong handson expertise on Kubernetes (Or AKS) Hands-on experience with MS Azure and its AI/ML services, including Azure ML Flow. Proficiency in using DevContainer for development. Knowledge of CI/CD tools such as Jenkins, GitHub Actions, or Azure DevOps. Experience with containerization and orchestration tools like Docker and Kubernetes. Strong problem-solving skills and the ability to work in a fast-paced environment. Excellent communication and collaboration skills. Preferred Skills: Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn. Familiarity with data engineering tools like Apache Kafka, Apache Spark, or similar. Knowledge of monitoring and logging tools such as Prometheus, Grafana, or ELK stack. Understanding of data versioning tools like DVC or MLflow. Experience with infrastructure as code (IaC) tools like Terraform or Ansible. Proficiency in Azure-specific tools and services, such as: Azure Machine Learning (Azure ML) Azure DevOps Azure Kubernetes Service (AKS) Azure Functions Azure Logic Apps Azure Data Factory Azure Monitor and Application Insights