Machine Learning Engineer

As a Machine Learning Engineer, you will be responsible for the design and development of Machine Learning Systems, as well as refining and updating the existing ones. You will help to bring the best software development practices to the data science team and accelerate their work. You will test machine learning libraries to their limits, often adding new functionalities. This includes enabling the production deployment of code, testing, and tracking of accuracy metrics. You will be constantly searching for performance improvements and deciding which ML technologies will be used in a production environment. Must have: - Bachelor's/Master's degree in Engineering with 2-4 years of industry experience - Solid engineering and coding skills, ability to write high-performance, production-quality code in Python - Good understanding of Generics, OOP concepts & Design Patterns - Experience in building containers using Docker - Familiarity with NumPy, Pandas, Keras, Pytorch, TensorFlow, scikit-learn libraries - Working experience with any one of the orchestration engines - Airflow, Kubeflow, SageMaker, Data Bricks - Understanding of the ML engineering lifecycle and MLOPs - modelling techniques, feature engineering, feature selection, model training, hyperparameter tuning, model evaluation, model serving - Experience in deploying applications to Kubernetes Good to have: - Knowledge of Parquet, Apache Arrow, PySpark - Understanding of GPU architecture, Cudas, Rapids - Knowledge of SQL databases - Postgresql, Mysql, MSSql - Exposure to No SQL data stores such as MongoDB/ Redis/ ElasticSearch/ Cassandra About Noodle.ai: Noodle.ai's mission is to create a world without waste. Our products are focused on areas with high waste - factories and supply chains worldwide. We believe traditional rule-based software fails business leaders, so we work with our customers to leverage Enterprise Artificial Intelligence® to improve product quality, optimize manufacturing schedules, improve fill rates, and optimize product distribution. Solving these problems provides our customers with a competitive advantage while also reducing wasted energy, excessive CO2 emissions, and resource wastage generated by supply chain inefficiencies.