AI Developer Course for Software Engineers
Build RAG applications, AI agents, FastAPI services and deployable AI products in 3 months.
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.
- Duration
- 3 Months ·
- Live Online
- Projects
- 4 Projects ·
- Deployed Capstone
- Fee
- ₹40,000 ·
- Certificate
Book a Free Session
Discuss your goals with our AI engineering team
Course Overview
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 — without requiring a machine-learning background. This AI application developer course covers the full journey from Python to production deployment.
- 01
Python
Scripting, automation, and data handling fundamentals
- 02
APIs
Expose AI capabilities through REST and streaming endpoints
- 03
RAG
Ground responses in real documents with retrieval pipelines
- 04
Agents
Add tool use, memory, and multi-step planning to workflows
- 05
Deploy
Ship with tracing, evaluation, and production infrastructure
The practical journey: write code, expose it through APIs, ground it with retrieval, add agentic workflows, then deploy and evaluate the product.
Audience
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 Developers
Add AI features and RAG-backed APIs to the services you already build.
Full-Stack Developers
Connect UI, APIs, models, and workflows into one complete AI product.
Frontend Engineers
Build AI-native UX — chat, streaming, and grounded, user-facing features.
Engineers Moving into AI
Already ship software? Add RAG, agents, and deployment to your toolkit.
Engineering Proof
AI Developer Projects: RAG, Agents and Deployed Apps
Build portfolio-ready AI features across search, documents, evaluation, agents, and deployment — the kind of work employers expect from an AI application developer.
3-Month AI Developer Course Syllabus
Tools & Stack
AI Development Tools and Frameworks
A practical developer-first AI stack covering model APIs, retrieval workflows, agent frameworks, application building, and evaluation. The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.
OpenAI, Claude, Gemini
coreCall 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
LangChain
coreApp orchestration — tools, memory, and chains
LangGraph
coreReliable 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
Embeddings & retrieval
Turn documents into searchable meaning with chunking and metadata
Qdrant, Pinecone, Chroma
Vector stores for semantic and hybrid search
FastAPI
Production backend APIs for your AI features
Gradio / Streamlit
coreQuick MVP frontends to demo and validate an AI app before a full UI
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
RAGAS
coreMeasure 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
Mentorship
Mentorship, Code Reviews and Capstone Delivery
Every project you build gets reviewed by engineering mentors. The focus is on practical architecture, code quality, and your ability to explain what you shipped.
Application Architecture Reviews
Mentors review your project architecture — API design, retrieval pipeline structure, agent workflow boundaries, and deployment choices. You get specific feedback on where your design holds up and where it needs to change before it ships.
Code, Evaluation and Reliability Reviews
Each project gets a code review covering output validation, guardrails, error handling, and evaluation setup. Mentors check whether your RAG responses are grounded, whether your agent workflows have proper stopping conditions, and whether your tracing captures the right signals.
Capstone Deployment and Presentation Review
Your capstone goes through a final review covering deployment quality, documentation, and your ability to explain the architecture. You present the project, answer questions about trade-offs, and get feedback that mirrors what interviewers ask AI developer candidates.
Prerequisite: comfort with coding and basic software engineering. You do not need prior ML or data-science experience — this course adds AI on top of the development skills you already have.
Enrollment
AI Developer Course Fees, Schedule and Next Cohort
Course Fee and Payment Options
One-time payment
₹40,000
3 months · Live online · Capstone · Certificate
No hidden charges. Batch timings confirmed on call.
Live Schedule, Recordings and Weekly Commitment
Sessions are live, instructor-led, and delivered online. All sessions are recorded and available for review. Contact admissions for the next confirmed cohort date.
What the Course Fee Includes
Live instructor-led sessions — not pre-recorded videos
4 guided projects + a deployed production capstone
Course completion certificate from School of Core AI
Mentor code reviews and architecture feedback on every project
Career support: resume review, portfolio guidance, mock interviews
Session recordings available for review
Not sure if this is the right course?
Talk to our team — we'll help you figure out which track fits your goals.
Credential
Certificate, Portfolio and Career Support
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. The certificate is awarded after completing all projects and the deployed capstone.
Portfolio and Project Presentation
Each project you build — RAG search, document workflows, agent pipelines, and the deployed capstone — becomes a portfolio piece you can demo and link on your resume. Mentors help you frame the work for interviews so you can explain architecture decisions and trade-offs.
Career and Placement Support
Career support includes resume review, portfolio guidance, and mock interviews focused on AI developer roles. Placement assistance is provided to help you connect with relevant opportunities, but employment is not guaranteed.
This certifies that
Your Name
Has completed the
AI Developer Course
3-Month Project-Based Program · School of Core AI
Python · FastAPI · LangChain · RAG Pipelines · AI Agents · Tool-Connected Workflows · Multimodal AI · Production Deployment
Aishwarya Pandey
Founder & CEO
Date
Upon completion
Learner Feedback
Learner Projects and SCAI Reviews
Outcomes from developers who upskilled and shipped AI-powered features.
Backend Developer → Now building AI-backed APIs
Aman Sharma
“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.”
Frontend Developer → Now ships AI UI features
Priya Nair
“Earlier I could call an API and show output. Now I understand streaming responses, grounding answers with sources, and handling edge cases in the UI. It finally feels like a product feature, not a demo screen.”
Full-Stack Developer → Now integrates AI into apps
Rohit Singh
“The big learning was production thinking — rate limits, retries, logs, and cost tracking. Before this, AI felt unpredictable. Now I know how to make it reliable enough to ship inside real user flows.”
Software Developer → Now builds retrieval workflows
Mehul Patel
“I moved from basic automation scripts to building retrieval-based workflows that solve real tasks. The architecture breakdown helped me see where things fail in production and how to design around it.”
Product-Focused Developer → Now prototypes AI faster
Emily Carter
“I already worked with APIs, but this helped me understand how AI changes product design — latency, uncertainty, and user trust. That perspective was extremely practical.”
Software Engineer → Now designs safe workflows
Daniel Hughes
“AI started feeling like normal software engineering. Instead of treating models like magic, I learned how to wrap them with validation, guardrails, and observability so teams can actually rely on it.”
Backend Developer → Now building AI-backed APIs
Aman Sharma
“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.”
Frontend Developer → Now ships AI UI features
Priya Nair
“Earlier I could call an API and show output. Now I understand streaming responses, grounding answers with sources, and handling edge cases in the UI. It finally feels like a product feature, not a demo screen.”
Full-Stack Developer → Now integrates AI into apps
Rohit Singh
“The big learning was production thinking — rate limits, retries, logs, and cost tracking. Before this, AI felt unpredictable. Now I know how to make it reliable enough to ship inside real user flows.”
Software Developer → Now builds retrieval workflows
Mehul Patel
“I moved from basic automation scripts to building retrieval-based workflows that solve real tasks. The architecture breakdown helped me see where things fail in production and how to design around it.”
Product-Focused Developer → Now prototypes AI faster
Emily Carter
“I already worked with APIs, but this helped me understand how AI changes product design — latency, uncertainty, and user trust. That perspective was extremely practical.”
Software Engineer → Now designs safe workflows
Daniel Hughes
“AI started feeling like normal software engineering. Instead of treating models like magic, I learned how to wrap them with validation, guardrails, and observability so teams can actually rely on it.”
Course Comparison
AI Developer Course vs Generative AI and Agentic AI
| AI Developer Course You are here | Generative AI Course | Agentic AI Course | LLMOps Course | |
|---|---|---|---|---|
| Best for | Developers building AI into real apps | Going broad into Generative AI | Agent orchestration & multi-agent systems | Production LLM serving & scaling |
| You focus on | APIs, RAG, agents, FastAPI, deployment | Models, prompting, multimodal, fine-tuning | Agent tools, planning, orchestration | Serving, observability, cost control |
| Prerequisite | You already code | Basic Python helpful | Some coding + RAG basics | Deployed an AI app before |
| You build | Production AI app features, end to end | LLM, RAG, agent & multimodal systems | Multi-agent workflows with tool use | Scalable LLM infrastructure |
| Leads to | AI Developer / AI App Developer | Generative AI Engineer | Agentic AI Engineer | LLMOps / AI Infrastructure Engineer |
FAQ
AI Developer Course Frequently Asked Questions
Clear answers for software developers exploring AI app development, RAG workflows, AI agents, modern frameworks, and practical implementation.
Talk to the School of Core AI Team
Questions about course fit, schedule, fees or career outcomes? Our academic counsellor will get in touch with you shortly.