Become a job-ready Agentic AI Engineer in 4 months
Learn Python, LLM APIs, RAG, and AI agents with LangChain, LangGraph and CrewAI, then deploy them on AWS. Live classes with working AI professionals, capstone projects and structured placement support.
- No coding background needed. We start from Python basics.
- Live classes 5 days a week, one phase per month, with an assigned mentor.
- Build AI agents and deploy them on real AWS cloud, then get resume, GitHub, LinkedIn and mock-interview prep.
- Live classes5 days a week
- AI agentsLangGraph · CrewAI
- Capstone projectsDeployed live
- Real AWS cloudBedrock · SageMaker
- Placement supportMock interviews & referrals
Go beyond prompting. Build AI agents that work.
Plain prompting is table stakes now. Employers are hiring for AI agents that can reason, use tools and complete multi-step tasks. This course goes straight to that skill.
Taught by working AI professionals
Live, mentor-led classes 5 days a week, starting from Python basics. No coding background needed.
Agents, not just chatbots
Design agents that plan multi-step tasks, call tools, remember context and recover when a step fails.
Deploy on real AWS cloud
Ship your agents and RAG apps on AWS Bedrock and SageMaker with Docker, the way real engineering teams do.
Real client work with Delta 360
Our sister IT company, Delta 360 Services, gives advanced students real client work to practise on, not toy examples.
A portfolio you can show
Deployed, mentor-reviewed capstone projects on GitHub, ready to walk recruiters through in interviews.
Placement prep built in
Technical resume for GenAI roles, GitHub and LinkedIn optimisation, mock technical interviews and job referrals.
The AI stack companies are hiring for
From LLM APIs and RAG to multi-agent frameworks and cloud deployment.
From beginner to AI engineer, one month at a time
Month 1 gives you Python and the working vocabulary of LLMs; Month 2 grounds those models in real data with RAG; Month 3 turns single responses into agents that plan, use tools and remember context; and Month 4 deploys everything on real cloud infrastructure. By the capstone, every earlier skill comes together in one deployed system.
Plain prompting is table stakes now. Employers are hiring for AI agents that can reason, use tools and complete multi-step tasks — this course goes straight to that skill, not around it.
- 1Month 1Foundations & LLMs
- 2Month 2Generative AI & RAG
- 3Month 3Agentic AI
- 4Month 4Cloud & LLMOps
- 5Final stretchCapstone & Placement
Tap a month to see every topic.
1Month 1Foundations & the LLM LandscapeWhere every AI Engineer starts: solid Python, and a working understanding of how today's LLMs actually behave.
- Python for AI: data structures, functions & working with APIs
- Git & GitHub version control workflows
- Prompt engineering fundamentals
- How LLMs work: training, inference & model families
- Working with OpenAI, Claude & Gemini APIs
- Choosing the right model for cost, latency & task
By the end of this month, every student can call production LLM APIs confidently, write prompts that produce reliable output, and reason about which model fits which task — the baseline every later month builds on.
Every AI feature a company ships starts with an API call, a well-built prompt and a sensible model choice. This month is where you learn to make all three decisions deliberately instead of by trial and error.
2Month 2Generative AI CoreMoving past single prompts into applications that ground LLM responses in real data.
- Transformer architecture, explained practically
- Advanced prompting: few-shot, chain-of-thought & structured output
- Fine-tuning open-source & foundation models
- Retrieval-Augmented Generation (RAG) pipelines
- Vector databases: Pinecone, FAISS & ChromaDB
- Capstone build: RAG-powered knowledge assistant
By the end of this month, every student has shipped a working RAG application — grounding an LLM in real data instead of relying on it to “just know” the answer. The first capstone build is delivered at month end.
The RAG-powered knowledge assistant covers upload, chunk, embed, retrieve and generate — the exact pattern companies use for internal help-desk bots and document Q&A, and the foundation your Month 4 capstone extends.
3Month 3Agentic AI EngineeringThe core of this program: moving from single-response GenAI to AI agents that reason, use tools and complete multi-step tasks autonomously.
- AI agent architecture: the observe → think → act loop
- Agents with LangChain & function / tool calling
- Stateful, graph-based agent workflows with LangGraph
- Multi-agent orchestration with CrewAI & AutoGen
- Agent memory & tool use: web search, code execution, custom APIs
- Guardrails, evaluation & failure-mode handling for agents
By the end of this month, every student can design agents that plan multi-step tasks, call external tools on their own, remember context across a conversation and recover gracefully when a step fails.
Most short GenAI courses stop at prompting. A production-grade agent needs state management, reliable tool-calling and failure handling — that depth is what separates a demo from an AI system a business can rely on.
4Month 4Cloud Deployment, LLMOps & CapstoneDeploy for real — the skills that separate a demo from a production AI system.
- AWS Bedrock & SageMaker for hosting foundation models
- Containerizing AI apps with Docker
- Serving agents & RAG apps via Streamlit / Flask
- Monitoring, logging & cost management
- Evaluation & guardrails for production AI systems
- CI/CD basics for shipping AI application updates
By the end of this month, the capstone app is live-deployed — every agent and RAG app you've built is deployed the way real engineering teams ship AI, on real cloud infrastructure. Placement preparation follows.
A model running only in a notebook on your laptop isn't a product. Deployment, monitoring and cost control are what hiring managers probe for when they ask whether you have shipped anything real.
5Final stretchCapstone Projects & Placement PrepReal, deployed AI builds, then turning that proof of work into offers.
- RAG-powered knowledge assistant (Month 2)
- Multi-agent capstone application: LangGraph + CrewAI + AWS Bedrock, deployed live
- Technical resume writing for GenAI / Agentic AI roles
- GitHub & LinkedIn portfolio optimisation
- Mock technical interviews & GenAI / agent system design
- Profile updates, job references & interview prep for each company
The capstone extends your Month 2 RAG assistant into a deployed, agentic workflow — multiple AI agents collaborating with tools, memory and orchestration. Each student then customises it into their own use case, deploys it live and adds it to a professional portfolio — their primary job-seeking asset.
Want the full syllabus with every topic?
Get the brochure PDF and a free call with a career counselor to plan your batch.
Capstone projects, deployed live
Real, deployed AI builds that become your portfolio, your main asset when you apply for jobs.
RAG-powered knowledge assistant
Upload, chunk, embed, retrieve and generate: the pattern companies use for internal help-desk bots and document Q&A. Built in Month 2.
Multi-agent AI application
Multiple AI agents collaborating with tools, memory and orchestration, using LangGraph, CrewAI and AWS Bedrock. Customised to your own use case and deployed live.
Advanced students also practise on real client work from our sister IT company, Delta 360 Services.
Train on real AWS cloud, free
The course is built around AWS's AI stack (Bedrock and SageMaker), and AWS offers free credits so you can practise on real infrastructure at no personal cost.
New accounts get $100 in credits immediately, up to $200 total within 6 months, plus 30 Always-Free services. No application needed.
Free, self-paced cloud and AI training from AWS, a solid companion track alongside your Qualibytes coursework.
Month 4 deployment labs on Bedrock and SageMaker, containerised hosting of your agents and RAG apps, and your live capstone deployment.
Credit amounts and eligible services are set by AWS and can change at any time. This is guidance from Qualibytes, not an official AWS publication; confirm current details at aws.amazon.com/free. “AWS” is a trademark of Amazon.com, Inc.
Beginner-friendly, built for the GenAI era
The course starts from fundamentals. Students are guided from zero to job-ready through structured, mentor-led months.
- Complete beginners with zero coding background
- College students and fresh graduates seeking AI-ready skills
- Software developers moving into AI/ML roles
- Working professionals upskilling for the GenAI era
- Data analysts wanting to move into machine learning
- Anyone wanting to combine coding with AI fluency
Placement support built into the course
Every enrolled student gets structured placement help: resume building, mock interviews and active job referrals. It is support, not a job guarantee: offers depend on your performance and the hiring company.
4 months of live training
By working AI professionals.
Live AI project work
Hands-on work on Delta 360's live AI projects.
Mock interviews
Practice and interview question prep.
Profiles & referrals
Resume, profile updates and job references.
*Institute-wide figure across all Qualibytes programs to date. Gen AI & Agentic AI Engineering is newly launched and has the same mentors, the same Delta 360 live-project pipeline and the same placement team. Ask your counselor for course-wise placement data.
Where our alumni work
Organisations where Qualibytes alumni have been placed to date. This is not a guarantee of a job at any specific company.
Career paths this course opens up
Salary range for Agentic AI roles (India, 2026)
Fresher with internship · 0–1 yr
₹8–13 LPAWorking dev + Agentic AI skills · 1–3 yrs
₹16–24 LPASenior Agentic AI Engineer · 3+ yrs
₹28–42 LPAIndicative Indian market ranges based on current industry benchmarks for Agentic AI roles. Actual offers vary by candidate, batch performance and hiring company.
Not sure if Agentic AI is right for you?
Talk to a Qualibytes career counselor. Free, no obligation. We will look at your background and goals.
A certificate backed by deployed projects
Your Gen AI & Agentic AI Engineering certificate is signed off only after a mentor reviews your capstone projects, so it tells a recruiter you can actually do the work.
Start in 4 simple steps
- Request a call FREEFill the short form or message us on WhatsApp.
- Talk to a counselorDiscuss your goals and timeline, the syllabus, fee, scholarships and EMI options.
- Pick your trackChoose online or classroom (Noida) and a batch timing that fits your schedule.
- Start learningLive training with an assigned mentor, capstone projects, then placement support.
Common questions
Do I need a coding background?
No. The course starts from fundamentals with Python, and mentors guide you month by month.
How long is the course, and how are classes held?
4 months of live classes, 5 days a week, with one phase each month: foundations and LLMs, Generative AI and RAG, Agentic AI, then cloud deployment and the capstone.
Is this only prompt engineering?
No. Prompting is covered in Month 1. Most of the course is about building RAG applications and AI agents with LangChain, LangGraph, CrewAI and AutoGen, and deploying them on AWS.
Do I have to pay for AWS or other software?
AWS offers free credits for new accounts that cover practice in the course. Amounts are set by AWS and can change; your mentor will show you how to set budget alerts so you don't overspend.
Is a job guaranteed?
No institute can honestly guarantee a job. Every student gets placement support: technical resume, GitHub and LinkedIn optimisation, mock technical interviews and job referrals. Offers depend on your performance and the hiring company.
Is this a new course?
Yes, it is newly launched. It is taught by the same mentors and supported by the same Delta 360 live-project pipeline and placement team as our other programs.
What is the fee, and is EMI available?
Your counselor will share the current fee, any scholarship you qualify for, and the payment options on your free call.
Where is Qualibytes located?
B-91, B Block, Sector 2, Noida, Uttar Pradesh 201301. Ask your counselor about classroom and online batches.
Your next 4 months start with one call
Talk to our admissions team about the curriculum, sample AI projects and a batch time that suits you.