Machine Learning Engineer

Proficiency with a deep learning framework such as TensorFlow or Keras

Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas

Expertise in visualizing and manipulating big datasets

Ability to select hardware to run an ML model with the required latency

Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress

Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability

Finding available datasets online that could be used for training

Training models and tuning their hyperparameters

Analyzing the errors of the model and designing strategies to overcome them

Deploying models to production