Job description
With the amount of data growing at an exponential rate, more and more businesses want to harvest the power of AI. To do so effectively and at scale, automated data pipelines and operationalized models are a necessity. The technical implementation of this offers huge challenges. Do you want to help businesses overcome these challenges? Find out what MIcompany has to offer and become a leader in the world of Data and AI engineering!
What will you do to make sure models can be used throughout an organization? In one project you could design and build the data platform architecture used for AI solutions. Using tools like Python, Airflow, Glue, or Jenkins you would, together with cloud engineers ensure, that there is a reliable data product that models can make use of. In a different project you could focus on deploying a model behind a endpoint, using tools like Sagemaker, Azure ML, Dataiku or DataRobot. You would make sure that data can be scored by a containerized model from anywhere, either in batch…
