Course Objectives

What you'll set out to achieve

  • ✦Build applications on top of large language models
  • ✦Design retrieval-augmented and knowledge-grounded systems
  • ✦Architect and orchestrate multi-agent systems
  • ✦Build AI systems that are secure and reliable enough for production
Who This Is For

Target Audience

  • →Developers and working professionals who want to build AI systems, not just use them
  • →Foundational Program graduates ready to go technical
  • →Anyone targeting an AI/ML engineering role

Some programming experience helps — Python fundamentals are covered early in the track. New to AI? Start with the Foundational Program.

Program Structure

4 technical tracks

The core, hands-on engineering path for building real AI systems.

4
Tracks
Level 2A
Stage
Build
Goal
Technical
Path
TRACK 1

Building with LLMs

Applications powered directly by large language models.

TRACK 2

Retrieval and Knowledge Systems

Grounding AI outputs in real, retrievable knowledge (RAG).

TRACK 3

Agentic Architecture and Orchestration

Designing and coordinating multi-agent systems.

TRACK 4

Secure and Reliable AI Systems

Hardening AI systems against failure modes and security risks.

Technologies Covered

Tools & Frameworks

Industry-standard tools used by real practitioners — so what you learn here transfers directly to the job.

PythonOpenAI APILangChainHugging FaceGit & GitHub
Where This Takes You

Career opportunities

Graduates of Agentic AI Engineering are prepared for entry-to-mid-level roles including:

AI/ML EngineerAI Application DeveloperAutomation EngineerSoftware Developer
Ready to start?

Enroll in Agentic AI Engineering

Weekend and weekday batches available. Trial classes offered. Speak to us to find the right start point for you.

Book a free trial class
FAQs

Common questions

Do I need to know how to code already?

Some programming experience helps, but isn't strictly required — Python fundamentals are covered early in the track.

How is this different from AI Operations?

Agentic AI Engineering is the technical path — you build AI systems with code. AI Operations is the non-programming path — you apply and manage AI in your role without writing code.

Build production-grade AI systems.

LLMs, retrieval, agents, and security — the full technical stack for building with AI.