Become a job-ready Gen AI & Agentic AI Engineer
Go beyond using AI — build it. Learn to ship LLM applications, retrieval-augmented (RAG) systems and autonomous AI agents that reason, use tools and act, backed by real builds and interview coaching that carry you to the offer.
Still deciding? Talk to a mentor first.
A free 15-minute call to map your path to a job-ready Gen AI & Agentic AI Engineer — honest guidance, zero pressure.
- Personalised learning roadmap
- Full syllabus, batch dates & fees
- Scholarships & no-cost EMI options
- Placement & interview support
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The skill every team is
racing to hire for
Generative AI moved from demos to production in under two years, and the next wave — autonomous agents that plan, use tools and complete tasks on their own — is already reshaping how software gets built. Companies everywhere want people who can actually build these systems, not just talk about them. This course makes you that person: someone who ships LLM apps, RAG assistants and multi-agent workflows end to end.
Generative and agentic AI is now the fastest-growing area of hiring across software teams.
Autonomous agents that reason, use tools and act are moving from prototype to production.
End-to-end AI builds — RAG, tool-using and multi-agent — you can walk an interviewer through.
Prompting, retrieval, agents, orchestration, deployment and evaluation — the full AI-engineering skill set.
Built for four kinds
of people
Whatever you’re starting from, the course begins at fundamentals — including the Python you need — and takes you to a job-ready AI engineer building real agentic systems.
Students & freshers
Final-year students and recent graduates who want a genuinely in-demand skill and an AI portfolio before applying.
Developers moving into AI
Working developers who want to add LLM apps, RAG and agents to their toolkit and pivot into AI-engineering roles.
Working professionals
Analysts, testers and support engineers upskilling into AI. We cover the basic Python you need — weekend batches available.
Founders & builders
You want to build AI products or features yourself — and need the structure, projects and code review to do it properly.
Four phases, twelve modules,
one AI engineer
Every module ends in hands-on work — you build LLM apps, RAG systems and agents. Nothing here is theory-only.
Python & LLM foundations
Start from the Python you need for AI, then learn how large language models actually work and how to steer them reliably with prompts and structured outputs.
Python for AI
Weeks 1–3Python essentials·Data types & control flow·Functions & modules·Working with APIs & JSON·Virtual environments·Async basics·NumPy & pandas intro·AI pair-programming (Copilot)
Foundations of LLMs & transformers
Weeks 3–5How LLMs work·Tokens & context windows·Transformer & attention intuition·Pretraining vs instruction tuning·Temperature & sampling·Model families (OpenAI, Gemini, Anthropic, open models)·Capabilities & limits·Responsible AI
Prompt engineering & structured outputs
Weeks 5–7Prompt patterns·Few-shot & chain-of-thought·System vs user prompts·Structured JSON outputs·Schema-constrained responses·Function/tool schemas·Prompt versioning·Reducing hallucinations
Retrieval & RAG
Give models real knowledge. Learn embeddings, vector search and retrieval-augmented generation, then build production RAG with the leading frameworks.
Embeddings & vector databases
Weeks 7–9What embeddings are·Semantic similarity·Chunking strategies·Vector databases (pgvector, Pinecone)·Indexing & metadata·Similarity search & filtering·Hybrid search·Re-ranking
Retrieval-Augmented Generation (RAG)
Weeks 9–11RAG architecture·Ingestion pipelines·Retrieval + prompt assembly·Grounding & citations·Handling long documents·Advanced RAG patterns·Evaluating answer quality·Reducing hallucination with context
Building with LangChain & LlamaIndex
Weeks 11–13LangChain core concepts·Chains & runnables·Document loaders & retrievers·LlamaIndex data framework·Memory & conversation state·Streaming responses·Prompt & output parsers·Wiring a full RAG app
AI agents & orchestration
Move from answering to acting. Build agents that reason, call tools and use functions — then orchestrate several of them into reliable multi-agent workflows.
AI agents: ReAct, tool use & function calling
Weeks 13–15Agent loop fundamentals·ReAct reasoning + acting·Function & tool calling·Giving agents tools (search, code, APIs)·Planning & reflection·Memory·Error handling & retries·Guarding against loops
Orchestration with LangGraph & multi-agent systems
Weeks 15–18Graph-based orchestration·State machines for agents·LangGraph nodes & edges·Human-in-the-loop·Multi-agent roles & handoffs·Supervisor & collaborator patterns·Shared state·Reliability & control
Model Context Protocol (MCP) & tool integration
Weeks 18–20Why MCP matters·MCP servers & clients·Exposing tools & resources·Connecting agents to real systems·Standardised tool interfaces·Security & permissions·Integrating APIs, files & databases·Building your own MCP tools
Production, evaluation & career
Make AI apps you can trust and ship — fine-tuned where it helps, guarded, evaluated and observable — then build your capstone and get interview-ready.
Fine-tuning, guardrails & evaluation
Weeks 20–22When to fine-tune vs prompt vs RAG·Fine-tuning & PEFT/LoRA intuition·Hugging Face models·Guardrails & input/output validation·Safety & prompt-injection defence·Evaluation frameworks·LLM-as-judge·Test sets & regression checks
Deployment, cost & observability of AI apps
Weeks 22–24FastAPI for AI services·Packaging with Docker·Deploying to the cloud·Streaming & latency·Token & API cost control·Caching·Tracing & observability (LangSmith-style)·Monitoring in production
Agentic AI capstone & interview prep
Weeks 24–26Scope an end-to-end agentic product·Design & build with mentor review·Deploy your capstone·Git & GitHub portfolio·AI-engineering resume·System-design for AI apps·Mock interviews·Talking through your builds
Want the full module-by-module syllabus?
We’ll send the complete curriculum PDF along with batch dates and fee details.
You graduate with a
portfolio, not just notes
Four complete AI builds — from RAG assistant to multi-agent workflow to a deployed capstone — created the way real teams work: version controlled, reviewed and shipped.
Production RAG assistant
A chatbot that answers accurately from your own documents — ingestion, embeddings, vector search, grounded answers with citations.
Tool-using AI agent
An agent that reasons, calls tools and completes tasks — searching, calling APIs and taking actions with function calling and the ReAct loop.
Multi-agent workflow
Several specialised agents collaborating on a research or automation task, orchestrated with LangGraph and human-in-the-loop control.
Capstone — your own agentic AI product
Scope, design, build and deploy an end-to-end agentic AI application of your choosing, with mentor guidance and code review at every stage.
The complete
AI-engineering stack
AI & Generative AI
- OpenAI API
- Gemini API
- Anthropic API
- LangChain
- LangGraph
- LlamaIndex
- RAG
- Embeddings
- MCP
- Hugging Face
Agents & orchestration
- ReAct
- Function calling
- Tool use
- Multi-agent systems
- LangGraph
- Human-in-the-loop
Language & APIs
- Python
- FastAPI
- REST & JSON
- Async
- Pydantic
- NumPy & pandas
Vector & data
- pgvector
- Pinecone
- PostgreSQL
- Chunking
- Hybrid search
- Re-ranking
Evaluation & safety
- Guardrails
- LLM-as-judge
- Evaluation sets
- Prompt-injection defence
- Fine-tuning & LoRA
- Observability
DevOps & tools
- Git & GitHub
- Docker
- Cloud deploy
- Postman
- VS Code
- GitHub Copilot
Roles you can apply for
when you finish
One skill set, several of the fastest-growing titles in tech — the AI-engineering roles teams are scrambling to fill.
Generative AI Engineer
Design and ship LLM-powered features and products into production.
Agentic AI Developer
Build autonomous agents and multi-agent workflows that reason, use tools and act.
LLM Engineer
Work close to the model — prompting, RAG, fine-tuning, evaluation and guardrails.
AI Application Developer
Integrate generative AI into real applications with clean APIs and interfaces.
AI Solutions Engineer
Turn business problems into AI systems — RAG assistants, agents and automations.
Prompt / AI Engineer
Design reliable prompts, structured outputs and evaluation pipelines for AI systems.
Resume & portfolio
We rewrite your resume around your AI builds and set up a GitHub that reads well to a reviewer.
Interview drills
LLM fundamentals, RAG, agents, system-design and prompting question banks, practised until the answers are automatic.
Mock interviews
Technical and HR rounds with working AI engineers, followed by honest feedback.
Hiring introductions
Your profile shared with our hiring-partner network as suitable AI roles open up.
Pick a batch
that fits your week
EDIT: replace the dates and timings below with your actual batch schedule.
Weekday morning batch
Weekday evening batch
Weekend batch — for working professionals
What every enrolment includes
- Live instructor-led sessions with recordings for revision
- Small batches with dedicated doubt-clearing time
- Four AI builds — RAG, agents and a deployed capstone — with mentor code review
- Resume building and mock interview rounds
- Placement assistance that continues after your last class
- Course completion certificate
Everything you’re
probably wondering
EDIT the answers to match DSU’s actual policies before publishing.
No. This is an AI-engineering course, not an AI-research one. You’ll build LLM apps, RAG systems and agents — there’s no calculus or linear-algebra prerequisite. We explain the concepts you need (embeddings, transformers, sampling) with intuition and hands-on code, not equations.
Basic programming helps, but it isn’t required — Module 1 covers the Python you need for AI from the ground up. If you’ve written a little code before, you’ll move faster; if you haven’t, the early weeks bring you up to speed before the AI modules begin.
The program runs 6–7 months and covers Python foundations, LLMs, RAG, agents, orchestration, MCP, evaluation and deployment. Weekday batches are two hours a day; weekend batches are three hours per day across Saturday and Sunday. Plan for a similar amount of build/practice time outside class — that’s where the learning consolidates.
It’s real and growing fast. Generative AI has moved from experiments into production across almost every industry, and agentic AI — systems that plan, use tools and act on their own — is the current frontier. Teams are actively hiring people who can build these systems end to end, and that supply is still scarce. This course is designed to make you one of those builders.
EDIT: state your fee here, along with any instalment or EMI options. Being upfront about pricing on this page will improve enquiry quality — people who can’t find a price often assume the worst and leave.
We provide placement assistance — resume preparation, mock interviews and introductions to our hiring-partner network — and that support continues after your final class. We don’t promise a guaranteed job, because no honest training provider can. What we can promise is that you’ll finish interview-ready with real AI builds to show. EDIT this to match your policy exactly.
You’ll work with leading model APIs (OpenAI, Gemini, Anthropic and open models via Hugging Face) and the frameworks teams use in production — LangChain, LangGraph and LlamaIndex — along with vector databases like pgvector and Pinecone, the Model Context Protocol (MCP) for tool integration, and FastAPI, Docker and cloud deployment to ship your work. The focus is on building and shipping, not just watching demos.
Yes. Book a free demo session and a one-to-one counselling call. You’ll meet the trainer, see how a session runs and get the full syllabus before committing to anything.
Every class is recorded and available to you, so you can catch up on your own schedule. You can also raise anything unclear in the dedicated doubt-clearing sessions.
Become a Gen AI & Agentic AI Engineer
Book a free counselling session. We’ll walk you through the syllabus, batch options and fees — and tell you honestly whether this course is right for you.