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Machine Learning Engineer

Roles & Responsibilities

  • Proficiency in Python and SQL, with experience using Linux and Git
  • Experience with ML frameworks and libraries including TensorFlow, PyTorch and Scikit-learn, Pandas, NumPy
  • Strong data processing and statistical analysis skills, including data cleaning and preprocessing
  • Excellent professional English communication skills and ability to translate business objectives into ML solutions

Requirements:

  • Collect data and transform it into analysis-ready formats; build and automate data collection tools
  • Explore large datasets to extract usable information; search, correlate, and derive actionable insights; create reports and presentations for business use
  • Research, design, and implement machine learning systems; select algorithms; train models, tune hyperparameters, and conduct experiments
  • Verify data quality through data cleaning; perform model testing and statistical analysis; translate business objectives into ML solutions

Job description

Essential Duties & Responsibilities & Skills Required:

-Collecting data through means such as analyzing business results or by setting up and managing new studies 

-Transferring data into a new format to make it more appropriate for analysis

-Creating new, experimental frameworks to collect data

-Building tools to automate data collection

- Searching through large data sets for usable information

-Creating reports and presentations for business uses

-Correlating similar data to find actionable results

-Solving difficult problems using ML-based algorithms.

-Research, design and frame Machine Learning Systems

-Understand and transform the prototypes of Data Science

-Verifying data quality, and/or ensuring it via data cleaning

-Perform Machine Learning Model tests and experiments

-Develop the Machine Learning Model as per the needs

-Perform the training models and tuning their hyperparameters

-Perform statistical analysis and fine-tune the testing results

-Choose and implement the right Machine Learning Algorithm

-Understanding business objectives and developing the ML Models

-Select the right training data sets for ML Model Development

- Professional English Level

Tools needed to be used: 

TensorFlow, Pytorch, Pandas, NumPy, Matplotlib, Seaborn, OpenCV, YOLOVx, Scikitlearn, CNN, RNN, Python, SQL, GIT, Linux

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