Generative AI App Developer Intern
Build powerful AI-driven applications before you graduate — and become the GenAI developer every company is looking to hire.
- Master Generative AI from Fundamentals to Deployment
- Build 3 AI-Powered Real-World Applications
- Learn Directly from AI Developers
- Placement-Oriented Learning Experience
About Program
The Generative AI App Developer Internship is designed for final-year VTU students who want to build real-world AI-powered applications and gain hands-on experience in LLMs, prompt engineering, and AI integration. The program covers the fundamentals of AI, embeddings, APIs, vector databases, chatbots, RAG, and multimodal models, along with no-code and low-code approaches. Students learn to build AI assistants, automation tools, content generators, and intelligent web apps suitable for modern industry workflows. With expert mentorship, project-driven learning, and placement preparation, students graduate with three portfolio-quality GenAI applications and the confidence to apply for AI developer, automation engineer, and product-focused roles.
Key Features
Program Content
Module 1 – Introduction to AI, LLMs & Modern AI Ecosystem
- Difference between ML, Deep Learning & GenAI
- Understanding LLMs: tokens, parameters, context windows
- API-based AI vs model training
- AI ecosystem: OpenAI, Gemini, Claude, LLaMA
- Types of GenAI apps: chat, generation, automation
- Zero-shot, one-shot, few-shot prompting
- Responsible AI & ethical considerations
- Understanding hallucinations & safety layers
Module 2 - Prompt Engineering & AI Interaction Design
- Prompt structure: instruction, context, constraints
- Task decomposition & multi-step prompting
- Prompt chaining & advanced prompting frameworks
- Role prompting & style prompting
- Using system instructions effectively
- Generating structured outputs (JSON, markdown, formats)
- Debugging prompts & improving reliability
- AI for coding, documentation & testing
Module 3 - Frontend & Backend Basics for AI App Integration
- JavaScript/TypeScript basics
- Fetching API data from AI models
- Rendering AI responses in web apps
- Basic React UI for AI chat interfaces
- Python basics for server-side AI workflows
- Building simple endpoints for AI apps
- Handling async operations & timeouts
- Deploying web apps (Netlify, Vercel)
Module 4 - No-Code & Low-Code AI Prototyping
- Replit basics for rapid ideation
- Building apps with Lovable AI
- Bubble.io workflows for AI apps
- Zapier/Make automations
- Creating UI flows without coding
- No-code API integrations
- Debugging and scaling no-code apps
Module 1 - Working with AI APIs (OpenAI, Gemini, Claude)
- API authentication & access keys
- Chat completion, text generation, embeddings
- Model selection: GPT-4, Gemini Pro, Claude Sonnet
- Rate limits, token usage & cost optimization
- Streaming responses for chatbots
- Error handling: timeouts, retries, failure patterns
- Logging & monitoring AI usage
- Secure API key management
Module 2 - Building AI Chatbots & Assistants
- Custom chatbot architecture
- Maintaining chat memory & context
- Building assistant personalities
- Handling long conversations with chunking
- Using function calling & tool use
- Adding speech-to-text & text-to-speech
- Context injection & grounding
- Deploying custom AI assistants
Module 3 - RAG (Retrieval-Augmented Generation) & Vector Databases
- Understanding embeddings
- Converting text → vector representations
- Creating an embedding store
- Using Pinecone / Chroma / Weaviate
- Querying and ranking documents
- Context retrieval for chatbots
- Building custom knowledgebases
- RAG pipelines using JavaScript or Python
Module 4 - Multimodal GenAI Apps
- Image generation: prompts, negative prompts, styles
- Vision-based AI: OCR, image-to-text
- Audio models: transcription, TTS
- Creating image captioning apps
- Creating AI-powered video tools (basics)
- Integrating multiple modalities into one app
- Combining image + text pipelines
Module 5 - AI Automation & Agents
- Agent architecture & planning
- Task decomposition using AI
- Agents using LangChain / LlamaIndex (intro)
- Workflow automations using AI + APIs
- Email, document, research automation
- Running background agents on cloud
- Scaling agent performance
Module 1 - Product Thinking for AI Applications
- Identifying real problems AI can solve
- Evaluating feasibility & cost
- User research & personas
- Mapping AI capabilities to use cases
- Designing intuitive AI experiences
- Understanding UX for AI apps
- Constraints & risks in AI product design
Module 2 - Startup & Innovation with GenAI
- Using AI to brainstorm product ideas
- AI-driven market research & competitor analysis
- Creating pitch decks using AI
- Landing page creation
- Building MVPs fast with no-code + AI
- AI monetization models
- Building AI tools for businesses
Module 3 - Professional Growth & Career Skills
- Resume optimization for AI roles
- LinkedIn branding for AI
- GitHub portfolio showcasing AI apps
- Preparing for AI developer interviews
- Communication for product demos
- Documentation for AI systems
- Presentation + storytelling for AI apps
Mini Projects (Examples)
- AI-powered note generator
- Keyword extractor & summarizer
- Prompt-enhanced content generator
- AI-based email reply assistant
Concepts Covered:
- Prompt engineering
- API calls
- Basic UI
- Streaming responses
Intermediate Projects (Examples)
- Custom chatbot with memory
- RAG-based knowledge assistant
- Image generation web tool
- Meeting transcription + summary app
Concepts Covered:
- Embeddings
- Routing
- Vector search
- Context retrieva
- Chat UIs
- Multimodal models.
Capstone Projects (Examples)
- Full Stack AI Assistant Platform
- AI Content Studio (blogs, scripts, captions)
- AI Resume + Portfolio Builder
- AI Research Agent for academic tasks
Concepts Covered:
- Architecture
- Full-stack development
- Multimodal integration
- API orchestration
- Deployment
- Documentation
- Scalability
Tools & Softwares
Why Choose This Internship
Salary Scale
Job Role
- Generative AI App Developer
- AI Automation Engineer
- AI Chatbot Developer
- Prompt Engineer
- Full Stack + AI Developer
- AI Product Engineer
- No-Code AI Developer
- RAG Developer
FAQ's
Yes. You'll receive VTU-compliant certificates and documentation.
No. The program starts from fundamentals and scales gradually.
AI assistants, content generators, RAG apps, and a full-stack AI product.
Yes — resume, LinkedIn, GitHub, mock interviews & job guidance.
Offered in both offline and hybrid formats.
Yes, you will receive a verified completion certificate from Rooman Technologies upon meeting all requirements.
Absolutely. The curriculum is beginner-friendly and AI-assisted.
OpenAI, Gemini, Claude, vector databases, LangChain, no-code tools
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
08069451000
Visit Us
Rooman Technologies, Bangalore, India