Machine Learning Ops Engineer

Machine Learning Ops Engineer is the one who’s 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 cost
• Envision, 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.

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