Skills & Experience
Deep understanding of Kubernetes internal architecture and its surrounding ecosystem, Service
mesh, CNI, CSI, operators, etc.
Hands on experience with working on Kubernetes extensions, building CRDs, operators using
Operator framework
Experience in designing && building realtime, large scale production-grade microservices
applications
Experience with production implementation of multiple SaaS and Kubernetes-based applications
including HA, DR, deployment strategies
Experience with scripting and automation is desirable (Bash, Terraform, Ansible, Jenkins, Chef)
Experience with container technologies (Docker, Kubernetes, flannel, CoreOS, etc)
Experience with multiple cloud providers (AWS, GCP, Azure, etc.)
Good Knowledge of full-stack infrastructure.
Working knowledge of multiple programming languages, like, NodeJS, Golang, Java, python
Build practices in MLOps that combine Machine Learning, DevOps, and Data Engineering, which
aim to deploy and maintain ML systems in production reliably and efficiently.
Model & Data Versioning Automated Version Control && tracking of model versions, along
with the data used to train it, and some meta-information like training hyperparameters.
Model Output && Data Validation Automating process of Model output & Data validation with
proper Metrics relevant to each model used in ML Algorithms
Monitoring Build & Production Systems using automated monitoring & alarm tools.
Keyskills: machine learning kubernetes azure python
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