Apprentice - 2
On-siteChennai, Tamil Nadu, India
Job Summary
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 analyzing large-scale distributed computing applications, utilizing tools such as Kubeflow, MLflow, and AutoML for the full ML Ops lifecycle. Conduct data preprocessing, feature engineering, and exploratory data analysis on network data, specifically within RAN and CORE domains, while optimizing hyperparameters to identify best-performing models. Deploy machine learning models using Keras, PyTorch, and TensorFlow, ensuring high performance and scalability on distributed systems with PySpark and Kafka. Prepare reports, visualizations, and presentations to communicate findings effectively to cross-functional stakeholders.
Required 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
- 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
- Strong problem-solving skills
- 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)
Desired Qualifications
- Experience with cloud platforms (AWS, Google Cloud, or Azure)
- Knowledge of MySQL/No SQL and Big Data ETL Pipelines
- Good understanding of time series analysis, data mining, text mining, and creating data architectures
- Utilize both batch processing and incremental approaches to manage and analyse 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 have a strong understanding of LLM models
- 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
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