Job Description

An AI/ML Intern assists in developing machine learning models, training neural networks, and optimizing AI algorithms. This role involves working with Python, TensorFlow, PyTorch, and data science libraries to create intelligent systems that solve real-world problems.

Key Responsibilities

1. Data Collection & Preprocessing

• Gather and clean data from APIs, databases, and datasets (CSV, JSON, SQL, etc.).
• Perform data wrangling, feature engineering, and data augmentation to improve model performance.
• Handle missing values, outliers, and normalization to ensure high-quality data input.

2. Machine Learning & Deep Learning

• Implement supervised and unsupervised learning models (Regression, Classification, Clustering).
• Train and fine-tune deep learning models using TensorFlow, PyTorch, Keras.
• Optimize hyperparameters and perform cross-validation to improve accuracy.

3. AI Model Development & Deployment

• Develop computer vision models (CNNs) for image recognition and object detection.
• Build NLP models (RNNs, LSTMs, Transformers) for text processing and chatbots.
• Deploy ML models using Flask, FastAPI, Docker, AWS, Google Cloud, or Azure.

4. Data Visualization & Model Evaluation

• Visualize model performance using Matplotlib, Seaborn, TensorBoard.
• Evaluate models with confusion matrix, precision-recall, F1-score, and ROC curves.
• Perform A/B testing to compare different AI models.

5. Research & AI Innovation

• Stay updated with the latest research in AI, ML, Deep Learning, and Generative AI.
• Experiment with GANs, Reinforcement Learning, and Explainable AI (XAI).
• Read research papers on Google AI, OpenAI, and arXiv to explore new trends.

6. AI Ethics & Responsible AI

• Ensure fairness, bias mitigation, and ethical AI in machine learning models.
• Implement data privacy and security best practices in AI applications.

Key Skills Required

Technical Skills:

✅ Programming Languages: Python (NumPy, Pandas, Scikit-learn), R (optional).
✅ ML & AI Libraries: TensorFlow, PyTorch, Keras, OpenCV, Hugging Face.
✅ Data Science Tools: Jupyter Notebook, Google Colab, Matplotlib, Seaborn.
✅ Model Deployment: Flask, FastAPI, Docker, AWS, Google Cloud, Azure ML.
✅ Big Data & Cloud Computing (Optional): Apache Spark, Google BigQuery.
✅ Version Control: Git, GitHub, GitLab.

Soft Skills:

✔️ Analytical Thinking: Ability to interpret complex datasets and AI models.
✔️ Problem-Solving: Finding innovative solutions using AI/ML techniques.
✔️ Attention to Detail: Ensuring model accuracy and fairness.
✔️ Communication: Presenting AI insights to non-technical teams.
✔️ Collaboration: Working with data scientists, software engineers, and business teams.