What You'll Do: Build and maintain cloud deployment of ML models and surrounding infrastructure Contribute to infrastructure and process improvements for data collection, labeling, model development and deployment Design and implement R&D data engineering solutions for delivery of ML model value from device to cloud, including message payload design, data ingest and database architecture Help prepare and automate builds for device model deployment Assist on projects led by other team members via data processing, programming, monitoring of production applications, etc. Other duties as assigned. What You'll Need: Bachelors degree in Computer Science or equivalent experience 4 to 6 years of experience with a Strong background in MLOps, Python, GNU/Linux CLI Versatile and adaptable engineer who can address evolving needs of team. Strong understanding of data engineering principles and architecture Knowledge of relational database modelling and integration. Experience with NoSQL is helpful. Ability to manage cloud resources and technologies within AWS, including Sage maker, EC2, S3 Experience with software automation tools, e.g., Airflow, Ansible, Terraform, Jenkins Experience with automated unit testing and regression testing methodologies Familiar with Linux software build toolchains and patterns. (e.g., Make, gcc) Experience with source control and tracking (git) Strong teammate who enjoys working in a collaborative, fast-paced team-focused environment