Apprentice - 2

Summary:

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Duties & Responsibilities:

Job Description Summary

• We are seeking a motivated, talented Machine Learning Intern to join our team and contribute to AI and ML projects. The internship gives an opportunity to work with experienced Data Engineers, Scientists, gain hands-on in developing and upgrading AI/ML models.
• The internship offers the candidate an excellent chance to apply his knowledge and learning on ML algorithms to solve real world problems in Telecom domain around Network Monitoring and troubleshooting, deploying trained models and improve the evaluation scores of the models.

Job Description

• Develop production-ready implementations of proposed solutions across different ML and DL algorithms, including testing on customer data to improve efficacy, and robustness.
• Research and test novel machine learning approaches for analysing large-scale distributed computing applications. Prepare reports, visualizations, and presentations to communicate findings effectively.
• End-to-End ML Ops Lifecycle: Implement and manage the full ML Ops lifecycle using tools such as Kubeflow, MLflow, AutoML, and Kserve for model deployment.
• Model Implementation: Develop and deploy the machine learning models using Keras, PyTorch, TensorFlow ensuring high performance and scalability.
• Distributed Systems: Run and manage PySpark and Kafka on distributed systems with large-scale, non-linear network elements.
• Proficient in Python programming and experienced with machine learning libraries such as Scikit-Learn and NumPy, Pandas.
• Good understanding of time series analysis, data mining, text mining, and creating data architectures.
• Processing Approaches: Utilize both batch processing and incremental approaches to manage and analyse large datasets. Conduct data preprocessing, feature engineering, and exploratory data analysis (EDA).
• Algorithm Experimentation: Experiment with multiple algorithms, optimizing hyperparameters to identify the best-performing models.
• Cloud Knowledge: Execute machine learning algorithms in cloud environments, leveraging cloud resources effectively.
• Model Retraining: Continuously gather feedback from users, retrain models, and update them to maintain and improve performance, optimizing model inference times.
• Network Domain Expertise: Quickly understand network characteristics, especially in RAN and CORE domains, to provide exploratory data analysis (EDA) on network data.
• Transformer Architecture: Implement and utilize transformer architectures and have a strong understanding of LLM models.
• Interact with a cross-functional team of data scientists, software engineers, and other stakeholders.
• 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.
• Knowledge of MySQL/No SQL and Big Data ETL Pipelines would be  an added advantage

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 and experience with AI/ML libraries such as NumPy, Pandas, Matplotlib, etc.
• 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).

Pre-Requisites / Skills / Experience Requirements: