AI Platform Engineer/ Data Quality & Automation / Hybrid in Chicago

About this kind of role

What a AI Platform Engineer/ Data Quality & Automation / Hybrid in Chicago usually does. The employer's own details are in the listing.

Typically does: This role focuses on building and maintaining the infrastructure that supports artificial intelligence and machine learning models. Responsibilities often include designing, developing, and deploying data pipelines to ensure data quality and reliability. Automation of data validation and monitoring processes is also a key aspect, alongside collaborating with data scientists and other engineers. They may also work on improving the scalability and performance of AI platforms.

Tools and skills: Proficiency in programming languages like Python, experience with cloud platforms (such as AWS, Azure, or Google Cloud), database management (SQL, NoSQL), and data engineering tools (Spark, Kafka) are commonly required.

Good fit for: Individuals with a strong background in data engineering and a passion for ensuring the quality and efficiency of AI systems often thrive in this role.