ML Engineer

Responsibilities

• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Managing available resources such as hardware, data, and personnel so that deadlines are met
• Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Verifying data quality, and/or ensuring it via data cleaning
• Supervising the data acquisition process if more data is needed
• Finding available datasets online that could be used for training
• Defining the preprocessing or feature engineering to be done on a given dataset
• Training models and tuning their hyperparameters
• Analyzing the errors of the model and designing strategies to overcome them
• Deploying models to production

• Proficiency with a deep learning framework such as Tensor Flow or Keras
• Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
• Expertise in visualizing and manipulating big datasets
• Proficiency with OpenCV
• Familiarity with Linux
• Ability to select hardware to run an ML model with the required latency

A Data Engineer designs, builds, and optimizes data pipelines, ensuring efficient data collection, storage, and proces

MLOps (Machine Learning Operations) Engineer focuses on streamlining the deployment, monitoring, and scalability of ma