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High—popular ML internship, metro location, and broad generalist skill requirements increase applicant density.
Medium—core ML skills transferable, but telecom network domain expertise increases domain specificity.
Medium—extensive ML/MLOps tooling expected but no explicit years requirement.
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Develop and deploy production-ready machine learning and deep learning models aligned with Telecom network monitoring and troubleshooting domains.
Manage the full ML Ops lifecycle including model deployment using tools like Kubeflow, MLflow, AutoML, and Kserve.
Collaborate with cross-functional teams to perform exploratory data analysis, optimize model performance, and maintain models through continuous retraining.
Currently pursuing or recently completed Bachelor’s/Master’s in Computer Science, Data Science, AI, or related field.
Proficiency in Python and experience with ML libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, and Pandas.
Familiarity with ML algorithms including supervised and unsupervised methods (e.g., regression, neural networks, RNN, LSTM, SVM).
Work Experience Required: Not explicitly mentioned in the JD.
Experience or strong interest in Telecom domain specifically related to RAN and CORE network data analysis.
Practical knowledge of ML Ops tools and cloud platforms (AWS, Google Cloud, or Azure) to manage model deployment and scaling.
Capable of end-to-end ML workflow from data preprocessing to algorithm experimentation and model retraining in a collaborative environment.