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AI Developer Course – Become a GenAI-Powered Developer

AI Developer Course with Python, FastAPI, LangChain, RAG and Agentic AI. Build production-ready AI applications and deploy on AWS. Mentorship, certification and job-readiness support included.

The AI Developer Course teaches you to build production-ready AI applications using Python, FastAPI, OpenAI, LangChain, and RAG. No ML background required. Master Agentic AI, secure tool calling with MCP, and cloud deployment on AWS.

India’s most practical, project-driven AI developer program with mentorship, certification, and job-readiness support. Download the syllabus or book a free expert call.

  • Build GenAI web apps (Gradio/Streamlit + FastAPI)
  • Production-grade RAG with evaluators & vector DBs
  • Agentic AI & multi-agent orchestration
  • AWS deployment, CI/CD & observability
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Inquire about our AI Developer Course

Top Reasons to Join Our AI Developer Course

Python + API Mastery

Master Python for AI development — from functions and data handling to REST APIs, FastAPI, and GenAI integrations.

GenAI Fundamentals

Grasp embeddings, attention, tokenization, and the architecture behind large language models used in real-world AI programming.

Prompt Engineering Skills

Learn prompt strategies like zero-shot, few-shot, chain-of-thought (CoT), and role prompting for better LLM responses.

Model API Ecosystem

Use GenAI APIs like OpenAI, Gemini, Cohere, and Mistral. Integrate them to build powerful AI apps using real dev workflows.

Build GenAI Web Apps

Deploy interactive GenAI apps using FastAPI, Gradio, Streamlit, or React — a must for every AI developer in production settings.

Retrieval-Augmented Generation (RAG)

Build RAG pipelines using LlamaIndex, FAISS, and ChromaDB. Combine document search with LLM responses for production use cases.

Multimodal GenAI

Work with text, image, and speech inputs using VLMs like Qwen-VL or LLaVA. Explore audio-chat, vision QA, and multimodal interfaces.

Agentic AI & Multi-Agent Apps

Build AI agents that plan, reason, and collaborate using CrewAI, LangGraph, and AutoGen. Apply to customer support or automation.

Mentorship & Capstone Projects

Get 1:1 mentorship while building capstone projects for your AI Developer portfolio. Launch to GitHub and prepare for job interviews.

Designed for backend engineers, app developers, and AI enthusiasts who want to build and deploy GenAI apps with Python, RAG, agents, and multimodal tools.

Python & FastAPI
LLM Prompt Engineering
RAG & Vector DBs
Multimodal GenAI Apps
Agentic AI & MCP
Enquire Now : +91 9997800680

Top Skills You’ll Gain in Gen AI for Developers

Python for GenAI Applications
Prompt Engineering & Optimization
OpenAI, Gemini, Claude API Integration
LangChain & LlamaIndex Fundamentals
RAG with PDFs, Web, and Databases
Multimodal AI (Image, Text, Voice)
Agentic AI Concepts & Tools (AutoGen, CrewAI)
Multi-Agent System Building
LangGraph & Function Calling
Memory, Tools, and Planning Patterns
Model Context Protocol (MCP)
Gradio & Streamlit for App Interfaces
FastAPI for Backend Integration
Deploying GenAI Apps on Vercel/AWS

Essential Tools You'll Master in This AI Developer Course

OpenAI API

LLM Access & Text Generation

Use GPT models via OpenAI’s API for chat, embeddings, and custom AI tasks. Integrate directly in your GenAI apps.

Gradio

UI for GenAI Prototypes

Quickly build and demo AI interfaces with minimal code. Perfect for deploying NLP, vision, or RAG tools.

LangChain

LLM Application Framework

Chain prompts, tools, and memory into powerful GenAI workflows using LangChain’s modular design.

LlamaIndex

Document RAG Framework

Enable advanced document search and retrieval with LlamaIndex. Connect LLMs to custom data sources like PDFs and websites.

Streamlit

Frontend for Python Apps

Build clean, interactive GenAI apps using only Python—no need for full-stack frameworks.

Prompt Engineering

Structured Prompting

Design better AI behavior using few-shot, zero-shot, and CoT prompts. Learn patterns for summarization, QA, and chat.

Multi-Agent Systems

Autonomous AI Agents

Use AutoGen, CrewAI, and LangGraph to build GenAI agents that collaborate, plan, and act independently.

MCP (Model Context Protocol)

Shared Memory & Tool Standard

Use MCP to manage memory, context, and tool sharing across multi-agent LLM systems at scale.

OpenAI API

LLM Access & Text Generation

Use GPT models via OpenAI’s API for chat, embeddings, and custom AI tasks. Integrate directly in your GenAI apps.

Gradio

UI for GenAI Prototypes

Quickly build and demo AI interfaces with minimal code. Perfect for deploying NLP, vision, or RAG tools.

LangChain

LLM Application Framework

Chain prompts, tools, and memory into powerful GenAI workflows using LangChain’s modular design.

LlamaIndex

Document RAG Framework

Enable advanced document search and retrieval with LlamaIndex. Connect LLMs to custom data sources like PDFs and websites.

Streamlit

Frontend for Python Apps

Build clean, interactive GenAI apps using only Python—no need for full-stack frameworks.

Prompt Engineering

Structured Prompting

Design better AI behavior using few-shot, zero-shot, and CoT prompts. Learn patterns for summarization, QA, and chat.

Multi-Agent Systems

Autonomous AI Agents

Use AutoGen, CrewAI, and LangGraph to build GenAI agents that collaborate, plan, and act independently.

MCP (Model Context Protocol)

Shared Memory & Tool Standard

Use MCP to manage memory, context, and tool sharing across multi-agent LLM systems at scale.

Gen AI For Developers Course Curriculum

Download Brochure

AI Developer Course Certificate

Upon completing the AI Developer Course, you’ll receive an industry-recognized certificate from the School of Core AI—validating your ability to build and deploy GenAI applications. From LLM integration and RAG pipelines to agentic systems, multimodal apps, and AWS deployment, this credential proves you’re job-ready.

ADVANCEDCERTIFIED
SCHOOLOFCOREAI
OF ACHIEVEMENT
This certificate is presented to
Shweta Sharma

Has successfully completed the AI Developer Course and demonstrated professional competencies in GenAI app development.

Aishwarya Pandey
Founder & CEO
DD/MM/YY
SCAI-AIDEV-000123

Gen AI for Developers vs Free Bootcamps

LLM App Development

✔ Build apps using OpenAI, Gemini, Claude & Hugging Face APIs.
✘ Limited to playground demos and basic text completion.

Multimodal GenAI

✔ Combine text, image, and speech models in real projects.
✘ Covers only basic text prompts; no multimodal tools.

RAG Pipelines

✔ Implement Retrieval-Augmented Generation with LangChain & LlamaIndex.
✘ No embeddings, vector DBs, or real-time search.

Agentic AI & Multi-Agent Systems

✔ Build agents using AutoGen, LangGraph, and CrewAI.
✘ No coverage of autonomous workflows or task chaining.

Frontend Integration

✔ Deploy GenAI apps using Streamlit, Gradio, or custom React UIs.
✘ CLI or notebook output only; no user-facing apps.

Production Deployment

✔ Use GitHub Actions, Docker, and API versioning for deployment.
✘ Lacks CI/CD or real-world deployment practices.

Certification & Job Support

✔ Skill Certificate + resume review + mock interviews + referrals.
✘ No certificate, mentorship, or placement support.

Gen AI for Developers – Course Fees

Build real-world GenAI applications—from LLMs and RAG to Agentic AI and multimodal tools. Get certified, mentored, and job-ready with one all-inclusive fee.
One-time Payment
₹45,000
₹45,000 flat – includes project-based GenAI training, certification, lifetime access, and placement assistance. No extra charges.

What You’ll Get:

  • Live mentorship by AI engineers and GenAI app builders.
  • End-to-end projects with LLM APIs, RAG pipelines, multimodal tools, and agent frameworks.
  • Resume & portfolio prep, mock interviews, and referral support.
  • Lifetime access to all recordings, updates, and future GenAI tools.
Enroll NowEnquire Now : +91 99978 00680

AI Developer Salary & Career Opportunities

Our AI Developer Course highlights real-world projects in RAG quality, agent reliability, evaluations, and AWS deployment — the exact signals employers use to determine AI developer salaries and promotions.

Salary Expectation in India

₹6–10 LPA (Entry)₹12–20 LPA (2–5 yrs)₹25–30 LPA+ (Advanced)

Freshers start around ₹6–10 LPA. With 2–5 years’ experience, roles like AI ML Developer or Full Stack AI Developer earn ₹12–20 LPA. Advanced profiles (RAG, Agents, AWS) can cross ₹25–30 LPA+.

Global Salary Trends

$110K–$160K (US)€70K–€120K (EU)

Globally, AI Developers and AI Engineers earn about $110K–$160K in the US and €70K–€120K in Europe, depending on stack (RAG, Agents), cloud expertise, and portfolio quality.

Roles You Can Target After the Course

  • • AI Developer / AI Engineer
  • • Python AI Developer / Full Stack AI Developer
  • • AI Application Developer (FastAPI + LangChain)
  • • RAG / Agentic AI Engineer
  • • Cloud AI Engineer (AWS)

Methodology: Salary data is based on public job listings, compensation reports, and typical career outcomes. Actual packages vary by skills, interview performance, and company profile.

What Our Learners Say

Real experiences from developers building future-ready GenAI apps

"Before this GenAI course, I had only dabbled with OpenAI APIs. By week 4, I had built a working RAG app using LangChain and Pinecone. The structured projects helped me understand not just how to use GenAI tools, but how to build actual products with them."
Ishaan Roy
Software Engineer, CodeMint Labs
"This was my first time working with speech and image models. The course guided me through building a multimodal interface using Whisper, Gemini, and Gradio. It was exciting to see real-time GenAI apps coming to life with minimal infra setup."
Sneha Kapoor
Frontend Developer, DeltaTech Systems
"I used to build basic automation scripts. Now I can deploy full-stack LLM apps with custom agents and function-calling. The Agentic AI module was a complete eye-opener. I even got my first freelance gig because of the capstone demo I built!"
Tanishq Verma
Junior Dev, Freelance AI Projects
"I was overwhelmed by all the GenAI hype until I joined this course. It breaks things down—first Python, then APIs, then full-stack projects. Now I can confidently use tools like LlamaIndex, Hugging Face, and deploy my own GenAI endpoints on Vercel."
Riya Sharma
Intern, NovaAI Labs
"What impressed me was the real-world focus. From token limits to cost optimization, everything was taught like you’re preparing for production. We used actual tools like LangGraph and LangServe—not toy examples. It felt job-ready."
Aarav Malhotra
Backend Dev, BrightByte Solutions
"Every week gave me something I could instantly try—whether it was connecting vector DBs or building a chatbot with persona memory. The Slack support and mentor check-ins helped me stay consistent and confident even as a beginner."
Divya Nair
Developer, MindMesh Studio

Other Gen AI Courses You Can Explore

Generative AI Specialization

Learn Generative AI from scratch. Covers LLMs, Fine-Tuning (LoRA, QLoRA), RAG, and Multimodal systems using OpenAI and open-source tools.

Explore Course

LLM Mastery Program

Deep-dive into transformer architecture, attention, Mixture of Experts, quantization, RLHF, model serving and deployment strategies.

Explore Course

Agentic AI Mastery

Master agentic AI. Build autonomous agents with LangGraph, AutoGen, CrewAI, and use Model Context Protocol (MCP) for real-world tasks.

Explore Course

Your Questions Answered – Gen AI Developer Course

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Enquire Now +91 9997800680 info@schoolofcoreai.com
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