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: 1. 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 2. Data & Database Engineering * NumPy * Pandas * Data Cleaning & Preprocessing * Matplotlib & Seaborn * SQL * PostgreSQL * Database fundamentals * Working with structured and unstructured data 3. Machine Learning * Scikit-learn * Supervised & Unsupervised Learning * Feature Engineering * Model Selection * Model Evaluation * XGBoost / Gradient Boosting * ML pipelines * Model interpretation 4. Deep Learning & AI * PyTorch * Neural Networks * CNN * Computer Vision * NLP fundamentals * Transfer Learning * Model training & evaluation * Hugging Face ecosystem 5. 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 6. 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. 7. 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**. 8. 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. 9. MLOps / LLMOps * MLflow * Model Versioning * Experiment Tracking * Model Deployment * Model Monitoring * LLM Evaluation * Prompt / response evaluation * Logging * Observability fundamentals * AI application performance monitoring 10. 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.”** 11. 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 12. 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 using the technologies you mentioned above? - Yes – Multiple end-to-end projects - Yes – 1–2 projects - Only academic/minor projects - Only tutorials/practice projects - No project experience yet * The AI Developer career pathway requires exposure to a broad technology stack — including Python, Data Handling, SQL, ML, Deep Learning, PyTorch/TensorFlow, GenAI, LLMs, RAG, Vector Databases, AI Agents, APIs, Git, Docker, Cloud and MLOps/LLMOps. Do you currently have working knowledge of most of these areas? - Yes – I have working knowledge of most of them - I know some of them but need training in the remaining areas - No – I am starting from the basics * If you do not currently have the required technical skills, are you genuinely interested in undertaking additional structured training to become job-ready in this field? - Yes – I am serious about building an AI/ML career and willing to undergo the required training - Yes – but I need to understand the program first - No – I am looking only for an internship * If your current technical skills do not meet the requirements for the internship, are you willing to undertake additional structured training to build the required skills? - Yes – I am serious about building a career in AI/ML and willing to undergo the required training - Yes – I would like to understand the training pathway first - No – I am looking only for an internship * Additional training, if recommended based on your assessment, may involve a program fee. If you are found suitable for the career pathway, are you willing to consider investing in the required training? - Yes – I am willing to invest in the required training - I would like to understand the program and fee structure first - No – I am looking only for a free internship * In a real software/AI company, technologies, frameworks, tools and development practices keep changing. As a working professional, would you be willing to continuously learn and adapt to new technologies whenever your role or project requires it? - Yes – I am comfortable continuously learning and adapting to new technologies - Yes – if it is required for my project or role - I prefer to work mainly with technologies I already know - No – I prefer a fixed technology stack * How serious are you about building a long-term career in AI/ML/Data Science? - Very serious – I want to build this as my career - Serious – I am actively preparing for an IT career - Interested – I am still exploring - Just looking for internship/certificate opportunities Work Location: In person