MLOps Engineer

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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:

• Hands on Experience in Azure DevOps practices and building release pipelines
• Proficient in Azure Cloud practices and managing Azure Kubernetes Service deployments
• Good to have Hands on experience in  Python or any other scripting languages (Groovy, Shell)
• Experience in Databricks, MLFlow deployment pipeline will be an added advantage
• Experience with monitoring tools like ELK, Prometheus, Grafana, App Insights etc
• Hands-on experience in Docker, Kubernetes.
• Excellent experience with source code management tools (git)
• Good to have 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.
• Good to have knowledge on Dataiku devops.