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.