Machine Learning Ops Professional

• Machine Learning Ops Engineer is the one whos responsible for managing and optimizing the deployment and operation of machine learning models in production environments
• He/she works closely with data scientists, software engineers, and DevOps teams to ensure that machine learning models are integrated seamlessly into existing systems and can scale effectively

Role & Responsibilities

• Responsible for design and implementation of secure and scalable infrastructure in Azure cloud
• Build and maintain CI/CD/CT pipeline across azure cloud platform for Data science projects
• Own and automate the infrastructure provisioning, demand forecasting and capacity planning
• Build tools and automation to improve systems availability, reliability, performance, monitoring and scalability
• Setting up the alerts and monitoring for the platform
• Monitoring system, application health, security controls and costEnvision, implement and rollout best MLOPs/DevOps tools and automation

Requirements:

• Strong understanding of concepts related to Machine Learning, Architecture, and MLOps practices
• Proficient in Azure Cloud practices and managing Azure Kubernetes Service infrastructure
• Hands on experience in Python or any other scripting languages (Groovy, Shell)Experience with monitoring tools
• Hands-on experience in Docker, Kubernetes
• Excellent experience with source code management tools (git)Experience with modern cloud development practices such as Microservices Architecture, REST Interfaces etc
• Experience in implementing Data/Model drift
• Strive for Continuous Improvement and build continuous integration, continuous development, and constant deployment pipeline
• Excellent Troubleshooting skills
• Always ready to learn more and adopt new cutting-edge technology with right value proposition