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Entry-level ML internship in a metro with a popular title increases applicant competition.
Core ML and MLOps skills are transferable, though telecom network domain knowledge increases specificity.
Many specific ML, MLOps, and framework requirements mean moderate filtering despite no explicit years.
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Develop and deploy production-ready machine learning and deep learning models for telecom network monitoring and troubleshooting.
Manage end-to-end ML Ops lifecycle including model deployment using tools like Kubeflow, MLflow, and Kserve.
Conduct data preprocessing, feature engineering, experiment with multiple ML algorithms, and optimize models using cloud environments and distributed systems.
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 (TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas).
Experience with ML concepts including supervised and unsupervised methods, and deep learning frameworks.
Work Experience Required: Not explicitly mentioned in the JD.
Has hands-on exposure to ML projects involving model deployment and ML Ops tools like Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
Familiar with telecom network domain especially RAN and CORE for applying ML to network data analysis.
Comfortable working in experimental and collaborative environments with strong focus on data preprocessing, algorithm tuning, and deployment pipelines.