Machine Learning Internship

Responsibilities: Design, train, and evaluate machine learning models for use cases like internship recommendation, resume parsing, fraud detection, and user engagement prediction. Perform end-to-end data workflows: data cleaning, feature engineering, EDA, and preprocessing using pandas, NumPy, and SQL. Build and fine-tune models using scikit-learn, XGBoost/LightGBM, and deep learning frameworks (PyTorch/TensorFlow) as needed. Develop APIs (Flask/FastAPI) to serve ML models in production and integrate them with web/mobile applications. Implement basic MLOps practices: experiment tracking, model versioning, logging, and monitoring. Work with LLMs and NLP pipelines for tasks like text classification, summarization, and semantic search over internships/resumes. Collaborate with full-stack developers to deploy models on AWS EC2 / cloud and ensure scalability and latency requirements. Document experiments, write clear reports, and present findings to the founding team. Requirements: Strong programming skills in Python and familiarity with data science libraries (pandas, NumPy, scikit-learn, matplotlib/seaborn). Solid understanding of ML fundamentals: supervised/unsupervised learning, evaluation metrics, overfitting, cross-validation, and feature engineering. Hands-on experience with at least one ML project (academic, Kaggle, internship, or personal) with code on GitHub. Basic knowledge of SQL and working with structured/unstructured data.