Key Responsibilities
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Develop, train, test, and optimize machine learning models for real-world applications.
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Work with Python libraries such as NumPy, Pandas, and Scikit-learn for data preprocessing, exploratory analysis, and model building.
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Apply ML techniques including regression, classification, and clustering based on project requirements.
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Implement mathematical/statistical concepts (probability, linear algebra, and statistics) to improve model performance.
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Collaborate with cross-functional teams to integrate ML solutions into products.
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Use Git for version control and code collaboration.
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Participate in model deployment pipelines, preferably using Flask/Django for API development.
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Work with SQL databases for querying and managing datasets.
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Explore and experiment with deep learning frameworks (TensorFlow/PyTorch) when required.
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Stay updated with the latest advancements in AI/ML and contribute innovative ideas.
Required Skills
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Strong Python programming capabilities.
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Hands-on experience with ML libraries: NumPy, Pandas, Scikit-learn.
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Good understanding of machine learning algorithms and workflows.
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Strong mathematical foundation: Statistics, Linear Algebra, Probability.
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Experience with Git for version control.
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Basic knowledge of:
Deep Learning (TensorFlow / PyTorch)
Web frameworks (Flask / Django)
Databases (SQL)
Cloud platforms (preferred)
Education
Bachelor’s degree in Computer Science, Information Technology, or a related field.