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PYTHON FULL STACK + GENAI & AGENTIC AI

Advanced AI & ML
Certification Program

AI & ML Training in Hyderabad

A 7-month Advanced AI & ML Certification Program, prepared by an IITian & AI Architect, that takes you from Python full-stack foundations through Machine Learning, Generative AI and Agentic AI to production-grade AI systems. Seven hands-on modules and a two-month real-time internship — built to get you job-ready as an AI engineer, not just an AI tool user.

Course Duration
7 Months
Core Learning
5 Months
Real-Time Internship
2 Months
Course Fees
₹70,000 / ₹75,000
Online / Offline
View Module Roadmap
Advanced AI & ML Certification Program skill map — AI Agents, Computer Vision, Machine Learning, Deep Learning, Real-World Projects, Data Science and Analytics, MLOps, Python for AI/ML, Generative AI, LLMs
Industry-Aligned
By an IITian & AI Architect
“
AI is the new electricity.
— Andrew Ng, Co-founder of Coursera & deeplearning.ai

Get the AI & ML Course Fee Structure & Syllabus

Share your details and our admissions team will call you back with the full syllabus, batch timings and fee breakdown.

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AI & ML Course Curriculum

Seven Modules. One Complete AI Engineering Skillset.

How can I become an AI/ML Engineer?

Get an Answer
01

Python & Technical Foundations

30 Hrs · 3 Weeks{<Explore Me />}
02

Python Backend Development with AI

40 Hrs · 4 Weeks{<Explore Me />}
03

Machine Learning Engineering

40 Hrs · 4 Weeks{<Explore Me />}
04

Generative AI & LLM Application Engineering

40 Hrs · 4 Weeks{<Explore Me />}
05

Agentic AI Engineering

40 Hrs · 4 Weeks{<Explore Me />}
06

MLOps, LLMOps & AI Platform Engineering

30 Hrs · 3 Weeks{<Explore Me />}
07

AI Solutions Engineering

40 Hrs · 4 Weeks{<Explore Me />}
Why This AI & ML Course

An AI & ML Course Built to Make You an AI Engineer, Not Just a Certificate Holder

Prepared by an IITian & AI Architect — curriculum built from real industry AI engineering practice

100% hands-on delivery — every module closes with a lab exercise or a real project, not slides

Learn to engineer AI, not just use AI tools — from Python full-stack foundations to production-grade systems

Structured path from backend engineering through Machine Learning, Generative AI and Agentic AI

Curriculum mapped toward globally recognised certification pathways (Azure, AWS, Google Cloud, NVIDIA and more)

A minimum of five portfolio projects across the program, documented to professional reporting standards

Real-time internship exposure across AI application development, MLOps and AI solutions delivery

Dedicated placement support — resume reviews, mock interviews and a hiring-partner network

Course Duration & Learning Path

Your AI Engineering Training Timeline, From Python to Production

260 hours of core curriculum across seven modules, followed by a two-month real-time internship.

01
Python & Technical Foundations
3 Weeks · 30 Hrs
02
Python Backend Development with AI
4 Weeks · 40 Hrs
03
Machine Learning Engineering
4 Weeks · 40 Hrs
04
Generative AI & LLM Application Engineering
4 Weeks · 40 Hrs
05
Agentic AI Engineering
4 Weeks · 40 Hrs
06
MLOps, LLMOps & AI Platform Engineering
3 Weeks · 30 Hrs
07
AI Solutions Engineering
4 Weeks · 40 Hrs
C
Capstone
End-to-End AI Solution
Final Module Project
I
Final Phase
Real-Time Internship
2 Months
AI & ML Tools

Python, PyTorch, LangChain & the Tools You'll Master

The complete toolset used across the program — from Python foundations to Generative AI, Agentic AI and production MLOps.

Python

Core programming language used across every module, from scripting to AI model development.

VS Code

Primary code editor used for writing, debugging and testing Python and AI application code.

Git

Version control system used to track code changes throughout every project.

GitHub

Code hosting and collaboration platform used for version control, CI/CD and portfolio building.

Linux

Command-line environment used for development, deployment and server administration.

Postman

API testing tool used to build, test and debug REST API requests.

REST APIs

The standard interface pattern used to connect applications, services and AI models.

JSON

The standard data-interchange format used across APIs, configs and AI application payloads.

FastAPI

Modern Python web framework used to build production-grade backend and AI-serving APIs.

PostgreSQL

Relational database used to store structured application and AI system data.

MongoDB

NoSQL document database used for flexible, schema-less application data storage.

Redis

In-memory data store used for caching, background job queues and fast lookups.

Docker

Containerization platform used to package and deploy applications and AI services consistently.

GitHub Actions

CI/CD automation platform used to test, build and deploy code on every change.

Pytest

Python testing framework used to write and run automated unit and integration tests.

AWS

Cloud platform used to deploy, scale and host backend and AI services in production.

Azure

Cloud platform used for deployment, AI services and enterprise-grade hosting.

NumPy

Numerical computing library used for array operations and mathematical computation in ML.

Pandas

Data manipulation library used to clean, transform and analyse structured datasets.

Scikit-learn

Machine learning library used to build, train and evaluate classical ML models.

PyTorch

Deep learning framework used to build and train neural networks and deep learning models.

Jupyter

Interactive notebook environment used for data exploration, experimentation and model prototyping.

MLflow

Experiment tracking and model registry platform used to manage the ML model lifecycle.

LLM APIs

Large language model APIs used to integrate generative AI capabilities into applications.

Hugging Face

Model hub and library ecosystem used to access, fine-tune and deploy AI models.

LangChain

Application framework used to build LLM-powered applications with chains and tool integrations.

pgvector

PostgreSQL extension used to store and query vector embeddings for semantic search.

Vector DBs

Purpose-built databases used to store and retrieve embeddings for RAG and semantic search.

MCP

Model Context Protocol used to connect AI agents and LLMs to external tools and data sources.

LangGraph

Agent orchestration framework used to build stateful, multi-step AI agent workflows.

Cloud Platform

Cloud infrastructure used to deploy, scale and monitor AI systems in production.

Monitoring Tools

Observability tooling used to track performance, cost and reliability of AI systems in production.

AI Engineering Projects

Real AI Engineering Projects for Your Machine Learning Portfolio

Every module is reinforced with hands-on work — a minimum of five projects across the program, each added to your portfolio and resume.

Capstone

End-to-End AI Solution

Design and build a complete AI solution from requirements to deployment and live demonstration.

Included

Generative AI / RAG Application

Build LLM-powered apps with RAG, vector search and document understanding.

Included

Agentic AI Application

Build intelligent agents that use tools, make decisions and execute multi-step tasks.

What You'll Learn

What You'll Be Able to Build After This AI & ML Certification

  • Develop AI applications using Python
  • Build and deploy secure backend APIs & services
  • Build and evaluate Machine Learning models
  • Develop deep learning solutions
  • Build LLM-powered applications
  • Develop Retrieval-Augmented Generation (RAG) systems
  • Work with embeddings and vector databases
  • Build and orchestrate AI agents
  • Integrate external tools and APIs
  • Work with LangGraph and MCP
  • Deploy AI applications to production
  • Evaluate and monitor AI systems
  • Apply AI security and reliability practices
  • Design end-to-end AI solution architectures
AI & ML Career Paths

AI & ML Jobs: AI Engineer, ML Engineer & More

Backend & Software Engineering

  • Backend Developer
  • API Developer
  • Python Developer
  • Software Engineer

AI & Machine Learning

  • AI Engineer
  • Machine Learning Engineer
  • MLOps Engineer
  • Machine Learning Developer

Generative AI

  • Generative AI Engineer
  • GenAI Application Engineer
  • LLM Application Developer
  • AI Application Developer

Agentic AI

  • Agentic AI Engineer
  • AI Agent Developer
  • AI Automation Engineer
  • AI Workflow Developer

AI Production & Platform

  • MLOps Engineer
  • LLMOps Engineer
  • AI Platform Engineer
  • AI Infrastructure Engineer

AI Solutions

  • AI Solutions Engineer
  • AI Integration Engineer
  • AI Implementation Engineer
  • AI Consultant
Role courses

Train for a specific AI & ML role

The same programme, duration and fees, with the learning path arranged around one job role. Pick the role you want and see exactly what you will learn.

AI EngineerPython development · FastAPI services · Machine learning modelsView role course →Machine Learning EngineerFeature engineering · Scikit-learn modelling · Model evaluationView role course →Generative AI EngineerPrompt engineering · LLM API integration · Structured outputsView role course →MLOps EngineerDocker containers · GitHub Actions CI/CD · MLflow trackingView role course →AI Solutions EngineerRequirement gathering · Workflow mapping · AI solution architectureView role course →Backend DeveloperPython programming · FastAPI development · REST API designView role course →
Salary Positioning

AI & ML Engineer Salary in India & Globally — What to Expect

Figures are broad, indicative ranges for entry-to-mid-level roles and vary significantly by company, location, specialization and experience. They are not a guarantee of outcome.

India
₹4L – ₹12L / year

Typical entry-to-mid range for AI Engineer, Machine Learning Engineer and Backend Developer roles, rising with certifications and project experience.

Global
$65K – $115K / year

Typical entry-to-mid range for equivalent AI/ML engineering roles in mature international markets.

Certification Readiness

AI & ML Certifications This Course Prepares You For

The curriculum is structured to help prepare learners for the following external certifications, including Microsoft Azure AI Engineer and AWS ML Engineer.

Microsoft Azure AI Engineer
AWS ML Engineer / Cloud Practitioner
Google Cloud Professional ML Engineer
NVIDIA Generative AI & LLMs
Databricks ML / GenAI Engineer
Oracle Cloud Infrastructure AI
IBM AI / Generative AI Engineering
TensorFlow & Hugging Face Credentials
Registration, fees and eligibility for any external certification exam are managed directly by the respective certifying body and may change over time. Skill IT Education awards its own course-completion certificate upon successful completion of the program.

Start your AI engineering career with a structured, hands-on program

7 months total — 5 months of core learning across seven modules, plus 2 months of real-time internship.

Start with Module 1Full Roadmap

Enquire About the AI & ML Course

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