AI/ML Internship – Python, Data Science & Generative AI

This program is designed for candidates who want to prepare for AI Developer / AI Engineer / ML Engineer / Generative AI Developer roles.

This is NOT a basic Python or introductory AI internship

Candidates should be prepared to learn and work across the complete AI development stack. Knowledge of only one or two tools is not sufficient for this career pathway.

COMPLETE AI DEVELOPER SKILL STACK

Candidates will be trained across the following industry-relevant technologies and development areas

Programming \& Software Engineering

• Advanced Python
• Object-Oriented Programming
• Data Structures \& Algorithms fundamentals
• Exception Handling
• Modular \& Production-oriented Python
• Virtual Environments \& Package Management
• VS Code / Jupyter / Google Colab
• Debugging \& Testing fundamentals

Data \& Database Engineering

• NumPy
• Pandas
• Data Cleaning \& Preprocessing
• Matplotlib \& Seaborn
• SQL
• PostgreSQL
• Database fundamentals
• Working with structured and unstructured data

Machine Learning

• Scikit-learn
• Supervised \& Unsupervised Learning
• Feature Engineering
• Model Selection
• Model Evaluation
• XGBoost / Gradient Boosting
• ML pipelines
• Model interpretation

Deep Learning \& AI

• PyTorch
• Neural Networks
• CNN
• Computer Vision
• NLP fundamentals
• Transfer Learning
• Model training \& evaluation
• Hugging Face ecosystem

Generative AI \& LLM Engineering

• Generative AI fundamentals
• Large Language Models (LLMs)
• Prompt Engineering
• Context Engineering
• OpenAI / Gemini / Claude APIs
• Hugging Face Transformers
• Embeddings
• Tokenization
• Structured Outputs
• Function / Tool Calling
• Local LLMs with Ollama

RAG \& Vector Intelligence

• Retrieval-Augmented Generation (RAG)
• Document processing
• Chunking strategies
• Embeddings
• Semantic Search
• Vector Databases
• FAISS
• ChromaDB
• Pinecone / equivalent vector platforms
• Retrieval evaluation
• RAG optimization

RAG is no longer something we should present as an optional “advanced topic”; it is one of the major patterns used for enterprise GenAI applications.

AI Agents \& Modern AI Application Development

• AI Agents
• Agentic AI
• Tool Calling
• Multi-step AI workflows
• LangChain
• LangGraph
• LlamaIndex
• Agent memory \& state
• MCP fundamentals
• AI automation workflows

This is important because modern AI development is moving beyond simple chatbot/API demonstrations toward agentic applications and tool-using systems.

AI APIs \& Backend Development

• REST APIs
• FastAPI
• API development
• Authentication fundamentals
• Connecting AI models to applications
• Database + AI integration
• AI application backend development

FastAPI is particularly useful here because it connects the Python/AI layer with deployable applications rather than leaving students with notebook-only projects.

MLOps / LLMOps

• MLflow
• Model Versioning
• Experiment Tracking
• Model Deployment
• Model Monitoring
• LLM Evaluation
• Prompt / response evaluation
• Logging
• Observability fundamentals
• AI application performance monitoring

Deployment \& Cloud

• Git
• GitHub
• Docker
• CI/CD fundamentals
• AWS
• Cloud deployment
• AI/ML cloud services
• Model/API deployment
• Basic Kubernetes concepts

Docker, cloud and deployment are critical because an AI Developer needs to move beyond “my model works in Jupyter” to “my AI application can actually be deployed and consumed.”

AI Evaluation, Security \& Responsible AI

• LLM evaluation
• Hallucination detection
• Guardrails
• Prompt injection awareness
• Output validation
• Data privacy fundamentals
• AI security basics
• Responsible AI
• Monitoring \& error analysis

Production-Level AI Projects

Candidates will work on projects that combine multiple components rather than isolated mini-projects.

Examples

• RAG-based AI applications
• AI chatbots
• AI agents
• Computer Vision applications
• Document Intelligence systems
• LLM-powered applications
• AI APIs using FastAPI
• AI applications connected to databases
• Deployed AI applications
• End-to-end ML/AI projects

THE EXPECTED AI DEVELOPER JOURNEY

The objective is not

Python → Certificate → Internship

Instead, the target is

Python → Data → ML → Deep Learning → PyTorch → GenAI → LLMs → RAG → Vector DB → Agents → APIs → MLOps/LLMOps → Docker → Cloud → Deployment → Portfolio → Interview Preparation

This is the level of technical breadth we want candidates to understand before applying for modern AI Developer-oriented career opportunities.

IMPORTANT FOR APPLICANTS

This opportunity is not intended for candidates looking only for a short-term certificate or a basic free internship.

Candidates applying should understand that modern AI Developer roles require a broad technical foundation.

If you are looking to build a serious career in AI/ML and are prepared to invest the required time in learning, practical work and projects, you are encouraged to apply.

This is a career-development/internship pathway and does not guarantee employment or placement.

Pay

₹1,000.00 - ₹2,000.00 per month

Application Question(s)

• Have you already completed any course/training in Data Science, AI/ML, Python, or a related technology field?

• Yes – Data Science
• Yes – AI/ML
• Yes – Python / Full Stack
• Yes – Multiple related courses
• No – I have not completed any formal training
• Which of the following technologies/domains have you already learned or worked with?

Select all that apply — Mandatory

• Python
• NumPy / Pandas
• SQL
• Data Analysis
• Statistics
• Machine Learning
• Scikit-learn
• Deep Learning
• TensorFlow / PyTorch
• NLP
• Computer Vision
• Generative AI
• LLMs
• Prompt Engineering
• RAG
• Vector Databases
• LangChain / LangGraph
• AI Agents
• FastAPI / REST APIs
• Git / GitHub
• Docker
• Cloud / AWS / Azure
• MLOps / MLflow
• None of the above
• Have you completed any technical projects