Kubevisor · Nov 11th 2020
A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. It will be a person that wants to innovate in the world of cloud big data engineering and machine learning. Major responsibilities include:
Understand the client's business need and guide them to a solution using Python, PyTorch, Kubeflow, Kubernetes, AWS etc.
Lead the customer projects by being able to deliver a machine learning project on AWS from beginning to end, including understanding the business need, aggregating data, exploring data, training models and deploying to AWS (EKS, S3 etc..) to deliver business impact to the organization.
You should have a detailed understanding of the ML lifecycle, including:
5+ years relevant work experience
Python to process data for modelling
Experience working with a wide range of predictive and decision models, including tools
ML workflow tools (e.g. Kubeflow/MLflow)
Developing end-to-end software projects
Experience using Linux to process large data sets
Experience with Kubernetes
Experience with AWS
Ideally you are in the GMT to GMT+4 timezone.