Machine Learning Internship

Overview

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)

What you'll do

• 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.

Who can apply

• Practical experience with Python, Flask, FastAPI, AWS.
• Experience level: 0-1 yrs.
• Strong written and verbal communication in English.
• Comfortable working on-site in online.

Skills