AIML�Intern /�AIML�Engineer�

Posted on April 16, 2025

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Job Description

  • ?����������Designation�:�AIML�Intern /�AIML�Engineer�
  • Machine Learning Engineer�Responsibilities:
  • ����������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. Determine and refine machine learning objectives.
  • ����������Designing machine learning systems and self-running artificial intelligence (AI) software to
  • automate predictive models.
  • ����������Transforming data science prototypes and applying appropriate�ML�algorithms and tools.
  • ����������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
  • ����������Ensuring that algorithms generate accurate user recommendations.
  • ����������Verifying data quality, and/or ensuring it via data cleaning.
  • ����������Supervising the data acquisition process if more data is needed.
  • ����������Defining validation strategies.
  • ����������Defining the pre-processing or feature engineering to be done on a given dataset
  • ����������Solving complex problems with multi-layered data sets, as well as optimizing existing
  • machine learning libraries and frameworks.
  • ����������Developing�ML�algorithms to analyze huge volumes of historical data to make predictions.
  • ����������Running tests, performing statistical analysis, and interpreting test results.
  • ����������Deploying models to production.
  • ����������Documenting machine learning processes.
  • ����������Keeping abreast of developments in machine learning.
  • Machine Learning Engineer Requirements:
  • ����������Bachelor's degree in computer science, data science, mathematics, or a related field.
  • �� � � � � Knowledge� as�a machine learning engineer. Proficiency with a deep learning framework
  • such as TensorFlow, XgBoost, Wavevnet, Keras,� numpy.
  • ����������Advanced proficiency with Python, Java, and R code writing.
  • ����������Proficiency with Python and basic libraries�for�machine learning such as scikit-learn and
  • pandas.
  • ����������Extensive knowledge of�ML�frameworks, libraries, data structures, data modeling, and
  • software architecture in ANN, CNN, RNN with LSTM.
  • ����������Ability to select hardware to run an�ML�model with the required latency
  • ����������In-depth knowledge of mathematics, statistics, and algorithms.
  • ����������Superb analytical and problem-solving abilities.
  • ����������Great communication and collaboration skills.
  • ����������Excellent time management and organizational abilities.

Required Skills

knowledge� as�a machine learning engineer. proficiency with a deep learning framework such as tensorflow xgboost wavevnet keras � numpy. ����������advanced proficiency with python java and r code writing. ����������proficiency with python and basic libraries�for�machine learning such as scikit-learn and pandas. ����������extensive knowledge of�ml�frameworks libraries data structures data modeling and software architecture in ann cnn rnn with lstm.