Agentic AI Applied Program

This program is designed for working professionals - software developers, full-stack engineers, and backend engineers who are already employed and want to add AI Engineering to their toolkit to access higher-value roles and higher compensation. The ideal candidate has 1+ years of professional experience writing code in Python; is comfortable with REST APIs, JSON, and client-server architecture; and has shipped at least one web application. You do not need prior machine learning or AI experience - working knowledge of what an LLM is and basic prompting familiarity is sufficient. If you write code for a living today, you are the right fit.

Admission to the program is direct. Eligible learners can apply by filling out the online application form, submitting the self-declaration and accepting the terms and conditions, and paying the program fee. Admission is confirmed once the payment is successfully completed.

You need working knowledge of GenAI concepts, prompting techniques, and common terminologies - but not formal machine learning or data science training. If you understand what an LLM is, how prompting works, and have built Python-based web applications, you have the foundation this program builds on.

NIIT follows a Mastery Learning Methodology — an immersive, sprint-based approach designed to build job-ready skills through hands-on practice and real-world problem-solving. The program is structured into learning sprints, where each sprint focuses on mastering a specific task aligned to industry job roles. Learners progress step-by-step through concept sessions, quizzes, practice assignments, reviews, feedback, and refinements. Each sprint requires approximately 4 hours of learning effort, with nearly 70% focused on hands-on implementation, practice, and refactoring. This structured methodology helps learners build confidence, gain practical experience, and apply skills effectively in real workplace scenarios.

AI Engineering is one of the fastest-growing and highest-demand skill areas for software professionals today. This program helps developers and engineers upgrade their existing programming and full stack skills with Agentic AI, RAG pipelines, LLM integration, and multi-agent orchestration capabilities that enterprises now require for production-scale AI systems. With hands-on projects, industry-relevant workflows, and production-focused learning, learners become job-ready for modern AI engineering roles where demand, hiring velocity, and compensation continue to grow significantly.

Generic AI courses are built for beginners and cover one tool or one concept at a time. This program is built for engineers - it assumes you already know how to code and jumps straight into production-grade concerns: RAG pipeline design, agentic patterns (ReAct, Plan-Act-Check, Reflection) as working implementations, multi-agent orchestration with LangGraph and CrewAI, and operational concerns (observability, guardrails, cost control) across 180 hours of structured, mentor-led, mastery-assessed learning. The capstone must meet defined SLOs with a live demo, not just function on a slide. If you are an experienced software professional, this is the program that will actually move your compensation and your role - not just add a certificate.

The capstone is an end-to-end agentic AI system designed to solve a real-world problem. It includes system design, implementation, evaluation against SLOs, and a live demonstration, along with supporting documentation such as a runbook.

The program maps directly to roles actively hiring in the agentic AI space: Agentic AI Developer, LLM Application Developer, RAG Systems Engineer, AI Orchestration Engineer, and AI Engineering Lead. These roles represent a step-change from traditional software development - both in scope and in compensation, typically 30–50% above equivalent senior software engineering positions. For working professionals already in software roles, this program provides the specific, demonstrable skill upgrade needed to transition laterally into these higher-demand, higher-compensation positions without leaving the engineering career track.

Learners complete course-embedded project builds - including a financial advisor agent, customer ticket resolution assistant, multi-modal RAG system, and job placement agent - plus a fully instrumented capstone project with a live demo, evaluation report against SLOs, and a production runbook. These are directly presentable to employers.

Learners need a laptop or desktop with Intel i3 or AMD Ryzen 3 (or higher), at least 8 GB RAM, and 50 GB free disk space, along with a functional webcam and microphone. The system should run Windows 10 or macOS (or higher) with the latest Chrome or Edge browser, MS Office or equivalent tools, and a PDF reader installed. A stable Wi-Fi or broadband connection with a minimum speed of 5 Mbps is required, and a backup internet connection is recommended.

All frameworks and infrastructure used- LangChain, LlamaIndex, LangGraph, CrewAI, Langfuse, Guardrails AI, and Postgres+pgvector - are open-source and free. No paid software subscriptions are required at any stage of the program.

No, prior experience with the specific frameworks is not required. The program teaches how to use these tools from a practical, implementation-first perspective. However, strong programming fundamentals and familiarity with APIs are expected.

Taking a loan is entirely optional. Loans are facilitated by third-party lenders, and NIIT has no role in the process. Applicants must review the loan terms carefully, including EMIs, interest rates, processing fees, and repayment schedules. All EMI payments and related queries must be handled directly with the lender.

Documents may include PAN Card and Aadhaar Card, last 6 months’ bank statements, 3 months’ salary slips (for employed learners) or ITR proof (for self-employed), and co-applicant details if the learner is under 21 years of age. Incomplete documentation may result in loan rejection.

Yes, learners receive a digital certificate after successfully completing the program and meeting all the required conditions (overall performance score, attendance, payment clearance etc).