Who should join this AI Developer Course?

This course is designed for backend developers, full-stack developers, frontend engineers building AI features, and software developers moving into AI-powered products. It is best suited for people who want to build AI applications and workflows inside real software systems.

Do I need prior machine learning or deep learning knowledge?

No. You do not need prior machine learning or deep learning experience. The course is built around practical AI development, API integration, retrieval workflows, and application-building rather than heavy mathematical theory.

What kind of applications will I build during the course?

You ship real builds: a RAG search app, a document-intelligence workflow with tool use, an evaluated RAG pipeline, an AI agent workflow, and a deployed full-stack capstone. Every project mirrors how AI features are actually built into products.

Will I learn RAG and AI agents in this course?

Yes. The course includes practical exposure to Retrieval-Augmented Generation (RAG) and agent-based workflows as part of modern AI application development. These topics are taught as useful building patterns for real products, not as isolated buzzwords.

Will I get exposure to modern AI frameworks and model APIs?

Yes. You work hands-on with LangChain, LangGraph, LangSmith, LlamaIndex, and AgentCore, plus model APIs from OpenAI, Claude, and Gemini, FastAPI backends, and vector databases like Qdrant, Pinecone, and Chroma.

Is this course more practical or theory-based?

This is a practical, implementation-focused course. The learning experience is centered on coding, guided builds, application patterns, and developer workflows rather than long theory-heavy sessions.

How is this AI Developer Course different from a Generative AI Course?

A Generative AI Course is usually broader and may cover concepts, models, prompting, and wider GenAI use cases. This AI Developer Course is more specifically focused on software developers who want to build AI applications using APIs, RAG, workflows, and practical implementation patterns.

Is this course suitable for working professionals?

Yes. The course is suitable for working professionals who want structured learning with practical guidance while continuing their existing job or development work.

What outcomes can I expect after completing the course?

After completing the course, you should be able to understand how modern AI applications are built, work with model APIs and retrieval workflows, contribute to AI feature development inside products, and present stronger practical AI work in interviews or technical discussions.

How is an AI Developer different from an AI Engineer?

An AI developer focuses on building and shipping AI features into applications — model APIs, RAG, agents, and deployment — using existing models. An AI or ML engineer leans more toward training, fine-tuning, and the underlying model and data pipelines. This course is developer-first: you ship AI products without needing to train models from scratch.

Is this a good fit for backend, frontend, or full-stack developers?

Yes. Backend, frontend, and full-stack developers are the core audience. You use your existing coding skills and add model APIs, RAG, AI agents, FastAPI backends, Gradio and Streamlit interfaces, and production deployment on top.

How long is the AI Developer Course and what does it cost?

It is a 3-month, live, project-based program. The fee is ₹40,000 (one-time), which covers live mentor-led sessions, four projects plus a deployed capstone, and a course completion certificate.

Do you provide placement support?

We provide career support such as resume reviews, mock interviews, portfolio guidance, and referrals where possible. This is structured support to help you become interview-ready rather than a guaranteed-job promise, and outcomes depend on your effort, projects, and background.

For software engineers moving into AI product development

AI Developer Course

Build RAG apps, AI agents, and deployable AI products.

This AI Developer course is for engineers who want a serious path into AI product work. In live mentor-led sessions, you build grounded search, tool-using agents, FastAPI services, lightweight UIs, evaluation checks, and a deployed capstone you can explain in interviews.

Turn private documents and data into grounded RAG search
Connect LLMs to tools, APIs, memory, and multi-step workflows
Package AI features with FastAPI, Streamlit, and Gradio
Deploy a capstone with tracing, evaluation, and documentation

Live engineering reviews4 builds + deployed capstoneCertificate on completion

Limited seats for the next cohort

Book a Free Session

Discuss your goals with our AI engineering team

Admissions open

Next live batch: 15th–30th

Limited seats (small batch)

Talk to us: +91 96914 40998

What Is an AI Developer Course?

An AI Developer Course trains software engineers to build AI features inside real applications: RAG search, tool-using agents, model API integrations, and deployed AI workflows. At School of Core AI, the path moves from Python and APIs to production-ready AI apps without requiring a machine-learning background.

Step 1

Python

Step 2

APIs

Step 3

RAG

Step 4

Agents

Step 5

Deploy

This is the practical journey: write code, expose it through APIs, ground it with retrieval, add agentic workflows, then deploy and evaluate the product.

Who Is This AI Developer Course For?

Built for backend, frontend, and full-stack engineers who want to add AI product work to their existing software skills.

Backend

Backend Developers

Add AI features and RAG-backed APIs to the services you already build.

Full-Stack

Full-Stack Developers

Connect UI, APIs, models, and workflows into one complete AI product.

Frontend

Frontend Engineers

Build AI-native UX — chat, streaming, and grounded, user-facing features.

Engineers

Engineers Moving into AI

Already ship software? Add RAG, agents, and deployment to your toolkit.

AI Developer Projects You’ll Build

Build portfolio-ready AI features across search, documents, evaluation, agents, and deployment — the kind of work employers expect from an AI application developer.

Tools, Frameworks, and Models You’ll Work With

A practical developer-first AI stack covering model APIs, retrieval workflows, agent frameworks, application building, and evaluation.

OpenAI, Claude, Gemini

core

Call frontier models for chat, reasoning, structured outputs, and tool use

Groq & Hugging Face

Fast inference and open models when you need speed, control, or lower cost

The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.

LangChain

core

App orchestration — tools, memory, and chains

LangGraph

core

Reliable multi-step and multi-agent workflows with routing and retries

LangSmith

Tracing, debugging, prompt versions, and evaluation runs

LlamaIndex

Data and document pipelines for retrieval

AgentCore

advanced

Run and scale agents as managed, production services

The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.

Embeddings & retrieval

Turn documents into searchable meaning with chunking and metadata

Qdrant, Pinecone, Chroma

Vector stores for semantic and hybrid search

The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.

FastAPI

Production backend APIs for your AI features

Gradio / Streamlit

core

Quick MVP frontends to demo and validate an AI app before a full UI

The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.

Docker

Containerize your app for consistent, repeatable deploys

AWS & Vercel

Host and scale your backend and frontend in the cloud

GitHub Actions

CI/CD so changes ship safely and automatically

The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.

RAGAS

core

Measure retrieval and answer quality in RAG apps

DeepEval

Automated checks for output quality and consistency

Tracing & monitoring

Track cost, latency, and failures with LangSmith / LangFuse

The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.

AI Developer Course Syllabus

12 sections|40+ modules|Projects hands-on|ILT mentor-led

Download Brochure

Sample certificate preview

Your AI Developer Course Certificate

Finish the AI Developer Course and earn a course completion certificate from School of Core AI — proof that you can build and ship real AI applications with RAG, AI agents, multimodal workflows, and production deployment.

CERTIFICATE

OF COMPLETION

THIS IS TO CERTIFY THAT

SCHOOL

CORE

YOUR NAME

Date : 25th Jan 26

Has Successfully Completed The

3-Month AI Developer Course (Project-Based)

Conducted By The School Of Core AI.

This Project-Based Program Included Hands-On Training In Python, FastAPI, Model APIs, RAG Pipelines, Vector Databases, AI Agents And Multi-Agent Systems, Multimodal AI, UI With Gradio And Streamlit, And Production Deployment Of AI Applications.

Aishwarya Pandey

Founder and CEO

Program :

AI Developer Course

School of Core AI

Share your certificate on LinkedIn, add it to your portfolio, or bring it to interviews as proof of the AI applications you built and deployed.

What Makes This AI Developer Course Different

Most AI tutorials hand you isolated notebooks and copy-paste snippets. This AI Developer Course takes you through the complete ecosystem — from basic Python all the way to a deployed, working AI solution — the way real engineering teams actually build and ship.

One connected path, not scattered scripts

You go from basic Python to a deployed AI solution as a single journey — APIs, RAG, agents, UI/UX, and deployment connected together, instead of isolated notebooks and copy-paste snippets.

The whole ecosystem, end to end

You learn how the pieces fit as one real system: model APIs, retrieval, multi-agent workflows, a usable frontend, and production deployment you can actually ship.

Built on your engineering strengths

You already know how to code. We add the AI layer on top of your software engineering skills, so you move faster than someone starting from zero.

A clear path to go deeper

Once you have a developer foundation, continue into Generative AI or AIOps tracks to specialize further — your next step is mapped, not guessed.

Prerequisite: comfort with coding and basic software engineering. You do not need prior machine learning, deep learning, or data-science experience — this course adds AI on top of the development skills you already have.

Want to go further afterwards?Generative AI Course AIOps Course View the full AI Developer roadmap

AI Developer Course Fees

Admissions open•Next batch: 15th–30th

One-time payment

₹40,000

3 months • Live ILT • Capstone • Certificate

All-inclusive

3 months

Live ILT

4 projects + capstone

Verifiable cert

No hidden charges. Batch timings confirmed on call.

Where This Course Can Take You

AI developers are some of the most in-demand engineers right now. Build the skills, ship the projects, and you open doors to roles like these:

AI DeveloperAI Application DeveloperGenAI Application DeveloperRAG DeveloperAI Product EngineerLLM Application Developer

Pay varies widely by city, experience, and what you can actually build — so we focus on making you genuinely build-capable, which is what moves offers. We don't publish inflated salary numbers.

What Developers Say

Real outcomes from developers who upskilled and shipped AI-powered features — without the hype.

Backend Developer → Now building AI-backed APIs

Aman Sharma

Systems > prompts

“I had tried LLM APIs earlier, but only for small experiments. Here I understood how to structure an AI feature like a real backend service — retrieval, evaluation checks, and fallback behavior. The shift was thinking in systems, not prompts.”