Machine Learning Engineer

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Job Description

Batch- 2023/2022 & earlier (Master’s Degree or Higher)


  • Build machine learning systems.
  • Offering Data Science Excellence to team in DS related modules.
  • Deeper understanding of DS methodology, approaches, tools and been able to formulate a business problem into a solution.
  • Transform data science prototypes into machine learning systems.
  • Build cost effective and efficient systems using parallel processing and other methods.
  • Run machine learning tests and experiments.
  • Perform Statistical Analysis and Fine-Tuning based on Test results.
  • Research and implement appropriate ML algorithms and tools.
  • Select appropriate datasets and data representation methods.
  • Train and retrain systems when necessary.
  • Collaboration with Data Engineering, BI, and other teams.

Experience Required:

  • Proven experience as ML engineer or similar role.
  • Experience in developing ML systems or ML products.
  • Understanding of data structures, data modelling and software architecture.
  • Deep knowledge of math, probability, statistics, and algorithms.
  • Ability to write robust code in Python and R.
  • Experienced in Big Data technologies like Spark and Hadoop.
  • Effective communication skills.
  • Involvement in business problem identification, proposing solutions and evaluating them, identifying required data sources, building data pipeline, visualizing the outputs, and taking actions based on the data outputs.
  • Experience in solving data science problems using machine learning, data mining algorithms and big data tools.
  • Strong Experience with at-least one programming language – e.g., Java, Python, R (Exposure to multiple is a plus)
  • Storing Experience in delivering data science projects leveraging cloud infrastructure.
  • Highly passionate about making an impact on business using data science.
  • Believes in continuous learning and sharing knowledge with peers.


  • Master’s degree or higher in one or more of the following quantitative disciplines, i.e., Mathematics, Statistics, Economics, Computing.