MLOps are creating a new intuitive way for Machine learning practitioners and software engineers to discover, build and deploy ML at scale.

MLOps are developing a state of the art platform for Data Scientists, Data Engineers, Machine Learning Engineers and Software Engineers to experiment, build and deploy ML models at scale using AWS. We provide our users with all the best practices of software engineering into the domain of data science, meaning we aim to incorporate data/model versioning, collaboration, monitoring etc. in an intuitive way that allows our users to prosper within the field.

As an ML Engineer in the MLOps team you will be a key stakeholder and owning a lot of responsibility in developing the backend on AWS to deliver world class practices within data engineering and machine learning modelling.

We utilize AWS Glue (Spark) and Sagemaker (TensorFlow, PyTorch, Scikit etc.) in combination with many other AWS services to deliver a SaaS product world wide to our customers accounts.

Experience with all frameworks and services are not necessary, but a clear understanding of distributed computing and machine learning is necessary, as well as knowing how to architect solutions on AWS overall.