AI Developer Internship
Go from Python basics to deployed AI applications with LLM APIs, RAG, agents and FastAPI, in twelve weeks of live lectures and weekly assessments.
- weeks
- 12
- live lectures
- 24
- proctored assessments
- 12
- reviewed deliverables
- 4
One Plan, Everything Included
Six months: the 12-week internship, then the career pathway, paid once.
- An AI-powered text utility in Python with structured JSON output
- An AI knowledge assistant with RAG, pgvector and source references
- A deployed AI capstone with Docker, security controls and documentation
- Working fluency with AI tools for coding, research and automation
Internship + pathway · 24 weeks of guided work
₹2,999
Paid once for 6 months, about ₹500 a month.
Start the 6-month planSign in with Google · Secure payment by Razorpay
Included
- A 12-week internship, then a 3-month career pathway
- 24 live lectures, two a week
- A proctored assessment every weekend, with your scores
- Three mentor-reviewed deliverables and a capstone defence
- Three career pathway projects, reviewed by a mentor
- LLM fine-tuning, multi-agent systems and production AI services
- Preparation for AI Engineer and GenAI Specialist roles
- AI literacy built into every month
- Offer letter emailed as soon as your payment is received
- Certificate of Internship and Letter of Recommendation on completion, both verifiable online
12 Weeks, Month by Month
Two live lectures a week, a proctored assessment every weekend, and a deliverable at the end of each month that a mentor reviews. AI tools are part of every month, along with how to check what they produce.
Month 1Weeks 1–4
AI Fundamentals & Python Programming
Introduce students to AI and teach Python programming, Git/GitHub, data processing, and basic LLM API integration from scratch.
8 lectures · 4 weekly assessments · 1 deliverable
Week 1 Weekend assessment
Lecture 01: What AI is, and isn't
Introduction to AI, AI vs ML vs Deep Learning vs Generative AI, real-world applications, limitations and conceptual understanding of LLMs
Lecture 02: Your development environment
Development environment setup, VS Code, Python installation, terminal, virtual environments, Git, GitHub, repository creation, commits, pushing and pulling code
Week 2 Weekend assessment
Lecture 03: Python fundamentals
Python fundamentals including variables, data types, operators, conditional statements and loops
Lecture 04: Functions and data structures
Functions, lists, tuples, dictionaries, sets and basic programming problem-solving
Week 3 Weekend assessment
Lecture 05: Modules, files and errors
Python modules, packages, pip, file handling, exceptions, JSON and environment variables
Lecture 06: Data with NumPy and pandas
NumPy, Pandas, CSV processing, data cleaning and basic data visualisation
Week 4 Weekend assessment
Lecture 07: APIs and your first LLM call
APIs, HTTP, REST, requests, JSON responses, API keys and first LLM API integration
Lecture 08: AI development foundations
AI development foundations, tokens, context windows, embeddings concepts, prompt fundamentals and structured outputs
Deliverable · Weeks 1–4
Month 1 deliverable: an AI-powered text utility in Python
AI literacy
- Understanding Generative AI concepts, leveraging ChatGPT for code learning and syntax explanation.
- Mastering prompt engineering fundamentals, analyzing and verifying AI-generated Python code, and avoiding unverified copy-pasting.
Month 2Weeks 5–8
Applied AI Development, RAG & Agents
Teach students how to build real-world AI applications including RAG systems, vector search, multi-step AI agents, and FastAPI backends.
8 lectures · 4 weekly assessments · 1 deliverable
Week 5 Weekend assessment
Lecture 09: LLM engineering
LLM engineering, model APIs, inference parameters, token costs, latency, structured outputs and error handling
Lecture 10: Prompt engineering for applications
Prompt engineering for applications, system instructions, few-shot prompting, prompt templates, output validation and prompt injection awareness
Week 6 Weekend assessment
Lecture 11: Embeddings and retrieval
Embeddings, semantic similarity, text chunking, vector databases and retrieval
Lecture 12: RAG architecture
RAG architecture, document ingestion, retrieval pipelines, grounding, citations and document question-answering systems
Week 7 Weekend assessment
Lecture 13: AI agents and tool use
AI agents, function calling, tool usage, agent loops, permissions and stopping conditions
Lecture 14: Stateful workflows with LangGraph
LangGraph fundamentals, stateful workflows, multi-step AI workflows, human approval and failure recovery
Week 8 Weekend assessment
Lecture 15: FastAPI backends for AI
FastAPI, REST endpoints, request validation and connecting AI systems to backend applications
Lecture 16: Evaluating AI systems
AI evaluation, test datasets, hallucination checks, retrieval quality, cost measurement and debugging
Deliverable · Weeks 5–8
Month 2 deliverable: an AI knowledge assistant with RAG
AI literacy
- Harnessing Cursor AI and GitHub Copilot for context-aware code completion, refactoring, and test generation.
- Auditing AI-generated prompts, stateful workflows, and vector retrieval pipelines for hallucinations and security vulnerabilities.
Month 3Weeks 9–12
Production AI Engineering & AI Productivity
Deploy production-ready AI applications with security, testing, and containerization, alongside mastering advanced AI developer productivity workflows.
8 lectures · 4 weekly assessments · 2 deliverables
Week 9 Weekend assessment
Lecture 17: Deploying AI applications
Deploying AI applications, FastAPI, Streamlit interfaces, Docker fundamentals, hosting and environment variables
Lecture 18: Securing AI applications
AI application security, authentication basics, API protection, prompt injection, data privacy, rate limits and spending limits
Week 10 Weekend assessment
Lecture 19: Production reliability
Production reliability, logging, monitoring, retries, caching, model selection, cost optimisation and fallback strategies
Lecture 20: Capstone engineering
Capstone engineering, GitHub workflows, code reviews, testing, deployment verification and live application demonstrations
Week 11 Weekend assessment
Lecture 21: Advanced prompting and context engineering
Advanced AI usage and prompt engineering, prompt structuring, context engineering, structured outputs, verifying responses, reducing hallucinations and managing long conversations
Lecture 22: AI-powered software development
AI-powered software development, Cursor, GitHub Copilot, repository-aware coding, implementation planning, code generation, debugging, refactoring, testing and AI-assisted code reviews
Week 12 Weekend assessment
Lecture 23: AI for everyday productivity
AI for everyday productivity, research, document summarisation, spreadsheets, presentations, professional emails, study workflows and task automation
Lecture 24: Advanced AI workflows and the final challenge
Advanced AI workflows, reusable prompts, selecting the right AI tools, multimodal AI, agent-assisted tasks, privacy, cost awareness, best practices and final productivity challenge
Deliverable · Weeks 9–12
Month 3 capstone: a deployed production AI application
Deliverable · Weeks 9–12
Month 3 deliverable: an AI productivity and automation suite
AI literacy
- Secure LLM API management, token budget management, rate limiting, and cost optimization.
- Implementing evaluation frameworks (e.g. Ragas), guardrails, prompt injection defenses, and privacy protocols.
Assessed Every Week, Not Just at the End
12 proctored assessments, one each weekend on that week's topics. You always know where you stand, and so does your mentor.
Every assessment, three parts
Part A: Multiple choice · 20%
Conceptual and theoretical questions on the week's topics.
Part B: Practical challenge · 60%
Hands-on coding, schema design or model building, based on the week's curriculum.
Part C: Code walkthrough · 20%
A short viva or recorded screen explanation of the decisions behind your implementation.
Proctored online
- Camera and Screen. Your webcam stays on and your whole screen is shared for the entire session.
- Your Setup. One monitor, a quiet room, and an identity check before you start.
- Academic Integrity. Zero tolerance for unauthorised AI help, plagiarism or switching tabs. A breach scores zero for that attempt.
Every Weekend
One assessment at the end of every week, on Saturday or Sunday, whichever you choose. Each runs 90 to 120 minutes, and attending is mandatory.
Pass With 50%
Score below 50% and you sit a re-evaluation within 48 hours, by Tuesday evening. The better of your two attempts counts.
Two Misses Mean a Mentor Review
Failing two weekly assessments in a row brings a mandatory review with your mentor, who looks at whether your track is the right fit.
70% for Your Certificate
Pass every weekly assessment and keep an overall average of 70% or more to present your capstone and earn the internship certificate.
The Tools You'll Work With
The stack working teams use today, including the AI tools, so nothing on your résumé is from a textbook.
- Core programming
- Python 3VS CodeGitGitHubpipvenvJupyter
- Data & AI foundations
- NumPyPandasMatplotlibPyTorchScikit-learnPydantic
- Generative AI
- OpenAI APIHugging FaceLangChainLangGraphSentence Transformers
- RAG & database
- PostgreSQLpgvectorSupabaseChroma
- Backend & testing
- FastAPIStreamlitREST APIsRequests/HTTPXpytest
- Deployment
- DockerRender / RailwayGitHub ActionsVercel
- AI productivity
- ChatGPTClaudeGitHub CopilotNotebookLMn8nPostman
After the Twelve Weeks: The Career Pathway
The rest of your six months, with your mentor: harder projects, open-source contributions and interview preparation.
Included in the 6-month plan
Month 4
Advanced AI Architecture & Fine-Tuning
Deep dive into LLM fine-tuning, advanced RAG optimization, hybrid search, reranking strategies, and model latency reduction.
Pathway project 1: fine-tuning and advanced RAG
Month 5
Autonomous AI Agents & Enterprise Workflows
Build multi-agent workflows with LangGraph, autonomous task execution, tool-calling safety boundaries, and custom enterprise integrations.
Pathway project 2: a multi-agent enterprise workflow
Month 6
Production AI Engineering & AI Role Preparation
Deploy production-scale AI microservices, manage LLM observability/evaluation pipelines, contribute to open-source AI frameworks, and prepare to move into AI Engineer / GenAI Specialist roles.
Pathway project 3: a production AI service and open source
About This Programme
Twelve weeks, from first principles to production. Month 1 introduces AI and teaches Python, Git and GitHub, data processing with NumPy and pandas, and your first LLM API calls. Month 2 is applied AI: prompt engineering, embeddings, RAG, agents with LangGraph and FastAPI backends. Month 3 takes AI applications to production with Docker, security, monitoring and cost control, and makes you fluent with AI tools for coding, research and everyday work.
Each month ends in a deliverable a mentor reviews: an AI-powered text utility in Python; an AI knowledge assistant that answers from your own documents; and a deployed production AI capstone, alongside an AI productivity and automation suite.
You start from scratch, so no AI experience is needed, and you learn to check what AI tools produce instead of copying it.
Common Questions
Why do I pay for an internship?
Because most of what you get is training. Structured lessons, mentor time and code reviews cost money to run. The internship is where you apply that training on real project work.
Will I get a stipend?
No. This is a training internship: you pay for the training and mentorship, and you get real project experience in return.
Do you guarantee a job or placement?
No, and be wary of anyone who does. What you will have is reviewed work, a portfolio and the experience to talk about it in interviews.
How long is the internship?
Six months, one plan. The first twelve weeks are the internship curriculum: 24 live lectures, a proctored assessment every weekend and three reviewed deliverables ending in a capstone. Months four to six are the career pathway: deeper projects, open-source work and interview preparation with your mentor.
Is it online?
Yes. Lectures are live online, and the weekly assessments are proctored online with your camera on and your screen shared. Tasks, submissions, scores and reviews live in your student portal, so you can join from anywhere.
When does it start?
With the next batch. As soon as your payment is received, you're placed in it and your offer letter is emailed to you with your start date. Your plan's weeks count from the batch's start date, so you don't lose any time while you wait. New tasks unlock every week.
Are there any extra charges at checkout?
No. The price on the site is the full amount you pay, with no taxes or fees added at checkout. Your receipt arrives by email.
Can I get a refund?
Internships are a digital service, so payments aren't refundable once your place is confirmed. Read the plan details before paying, and ask us anything first. The refund and cancellation policy covers the exceptions, such as a duplicate payment.
How am I assessed?
Every weekend you sit a proctored assessment of 90 to 120 minutes on that week's topics: multiple choice (20%), a practical coding or architecture challenge (60%) and a short walkthrough of your code (20%). The pass mark is 50%. Below that, you get a re-evaluation within 48 hours, and the better attempt counts.
Do I get a certificate and a letter of recommendation?
Yes. You get your offer letter when you pay, and a Certificate of Internship and a Letter of Recommendation when you complete the programme. The certificate needs every weekly assessment passed with an overall average of 70% or more, which also qualifies you to present your capstone. Every document is issued in your student portal with a QR code anyone can scan to verify it.