Machine Learning Intern

About the Role

We are seeking a motivated, talented Machine Learning Intern to join our team and contribute to AI and ML projects. This internship provides an opportunity to work with experienced Data Engineers and Scientists, gaining hands-on experience in developing and upgrading AI/ML models.

It offers an excellent chance to apply your knowledge of ML algorithms to solve real-world problems in the Telecom domain, specifically around Network Monitoring and troubleshooting, deploying trained models, and improving model evaluation scores.

Responsibilities

• Develop production-ready implementations of proposed solutions across various ML and DL algorithms, including testing on customer data to improve efficacy and robustness.
• Research and test novel machine learning approaches for analyzing large-scale distributed computing applications.
• Prepare reports, visualizations, and presentations to communicate findings effectively.
• Implement and manage the full MLOps lifecycle using tools such as Kubeflow, MLflow, AutoML, and Kserve for model deployment.
• Develop and deploy machine learning models using Keras, PyTorch, and TensorFlow, ensuring high performance and scalability.
• Run and manage PySpark and Kafka on distributed systems with large-scale, non-linear network elements.
• Utilize both batch processing and incremental approaches to manage and analyze large datasets. Conduct data preprocessing, feature engineering, and exploratory data analysis (EDA).
• Experiment with multiple algorithms, optimizing hyperparameters to identify the best-performing models.
• Execute machine learning algorithms in cloud environments, leveraging cloud resources effectively.
• Continuously gather feedback from users, retrain models, and update them to maintain and improve performance, optimizing model inference times.
• Quickly understand network characteristics, especially in RAN and CORE domains, to provide exploratory data analysis (EDA) on network data.
• Implement and utilize transformer architectures and demonstrate a strong understanding of LLM models.
• Interact with a cross-functional team of data scientists, software engineers, and other stakeholders.

Qualifications

• Currently pursuing or recently completed a Bachelor’s/Master’s degree in Computer Science, Data Science, AI, or a related field.
• Strong knowledge of machine learning concepts, algorithms, and deep learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
• Proficiency in Python programming and experienced with machine learning libraries such as Scikit-Learn, NumPy, and Pandas.
• Good understanding of time series analysis, data mining, text mining, and creating data architectures.
• Understanding and experience in working with supervised and unsupervised machine learning methods such as regression, neural networks, deep learning, RNN, LSTM, KNN, Naive Bayes, SVM, decision trees, random forest, gradient boosting, ensemble methods, and text mining.
• Hands-on experience with data preprocessing, feature selection, and model evaluation techniques.
• Familiarity with SQL and NoSQL databases for data retrieval and manipulation.
• Experience with cloud platforms (AWS, Google Cloud, or Azure) is an advantage.
• Strong problem-solving skills and ability to work in a collaborative team environment.
• Excellent communication and analytical skills.
• Previous experience with AI/ML projects, Kaggle competitions, or open-source contributions.
• Knowledge of software development best practices and version control (Git).
• Understanding of MLOps tools and model deployment techniques (Docker, Kubernetes, Flask, FastAPI).
• Knowledge of MySQL/No SQL and Big Data ETL Pipelines would be an added advantage.