Python AI Intern

Python AI Intern

Position Summary

We are seeking a motivated and talented Python AI Intern to join our AI/ML team. This internship offers hands-on experience in artificial intelligence, deep learning, and data science, working with state-of-the-art tools and real-world datasets.

Key Responsibilities

Assist in designing, developing, and deploying machine learning and deep learning models using Python.

Work with popular Python libraries and frameworks such as NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, Keras, Matplotlib, Seaborn, OpenCV, NLTK, SpaCy, Hugging Face Transformers.

Perform data collection, preprocessing, cleaning, transformation, and feature engineering.

Conduct exploratory data analysis (EDA) and data visualization.

Implement and optimize algorithms for classification, regression, clustering, recommendation, NLP (natural language processing), computer vision, and time series analysis.

Develop and evaluate models using cross-validation, hyperparameter tuning, and performance metrics (e.g., accuracy, precision, recall, F1 score, ROC-AUC).

Utilize Jupyter Notebook and Google Colab for prototyping and experimentation.

Integrate AI/ML models into web applications or APIs using Flask, FastAPI, or Django.

Employ SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB) for data storage and retrieval.

Use Git for version control and collaborate via GitHub/GitLab.

Participate in code reviews, documentation, and team meetings.

Required Qualifications

Current enrollment in a Bachelor’s or Master’s program in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Electrical Engineering, or related field.

Strong Python programming skills, including object-oriented programming and scripting.

Familiarity with machine learning algorithms (supervised, unsupervised, deep learning).

Experience with data wrangling, visualization, and statistical analysis.

Ability to work with large datasets and knowledge of Big Data concepts.

Understanding of RESTful API development and containerization (e.g., Docker) is a plus.

Exposure to cloud computing platforms (AWS, GCP, Azure) and MLOps concepts is desirable.

Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

Coursework or projects involving convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTM, transfer learning, transformers, BERT, GPT, or generative AI.

Experience with automation tools (e.g., Airflow, Prefect) and experiment tracking (e.g., MLflow, Weights & Biases).

Participation in Kaggle competitions or contributions to open-source AI projects.

Knowledge of Linux/Unix environments and shell scripting.

Duration & Location

Internship Duration: 3-6 months (potential for extension or conversion to full-time)

Location: On-site, remote, or hybrid (specify as applicable)

Benefits

Mentorship from experienced data scientists and machine learning engineers.

Exposure to advanced AI technologies and real-world applications.

Professional development and networking opportunities.