Job-ready learning

Basic

AI Engineering.

Practical AI development covering applications, APIs, automation, and AI integration into software systems.

Learner rating

4.9/5

Course duration

6 Months

Learning modules

13 modules

Learning mode

Online/ Offline

860+ learners enrolledIndustry-recognized certificate

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Program overview

AI Engineering is a 3 Months Artificial Intelligence program built around hands-on practice, guided assignments, interview preparation, and placement support. Start with Python, practise with Python, OpenAI APIs, LangChain, and connect your learning through applied business scenarios. Build a portfolio that explains your approach and prepare for roles such as AI Engineer and Generative AI Developer.

3 Months structured training program

Hands-on practice with Python, OpenAI APIs, LangChain

Live projects and portfolio-ready assignments

Certification guidance and placement assistance

A structured learning journey

AI Engineering Curriculum

Explore the curriculum, build your understanding one module at a time, and put each new skill into practice.

01Introduction to Artificial IntelligenceModule 1 · 1 topics

Introduction to Artificial Intelligence (AI) History and Evolution of AI Types of Artificial Intelligence AI Applications in Real-World Industries AI vs Machine Learning vs Deep Learning Introduction to AI Engineering AI Development Lifecycle

02Python Programming for AIModule 2 · 1 topics

Python Fundamentals Variables, Data Types and Operators Conditional Statements and Loops Functions and Modules Lists, Tuples, Sets and Dictionaries Object-Oriented Programming Exception Handling and File Handling Python Libraries for AI Development

03Mathematics & Statistics for AIModule 3 · 1 topics

Linear Algebra Fundamentals Vectors and Matrices Probability Concepts Descriptive and Inferential Statistics Mean, Median, Mode and Standard Deviation Probability Distributions Correlation and Regression Mathematical Concepts for Machine Learning

04Data Analysis & PreprocessingModule 4 · 1 topics

Introduction to Data Analysis NumPy and Pandas Data Collection and Data Loading Data Cleaning and Transformation Handling Missing Values and Outliers Exploratory Data Analysis (EDA) Data Visualization using Matplotlib and Seaborn Feature Engineering

05Machine LearningModule 5 · 1 topics

Introduction to Machine Learning Supervised and Unsupervised Learning Regression Algorithms Classification Algorithms Clustering Techniques Decision Trees and Random Forest Support Vector Machines (SVM) K-Nearest Neighbors (KNN) Model Training and Evaluation

06Advanced Machine LearningModule 6 · 1 topics

Ensemble Learning Gradient Boosting XGBoost and LightGBM Hyperparameter Tuning Feature Selection Cross-Validation Techniques Model Optimization Model Performance Evaluation

07Deep Learning & Neural NetworksModule 7 · 1 topics

Introduction to Deep Learning Artificial Neural Networks (ANN) Perceptron and Activation Functions Forward and Backpropagation Loss Functions and Optimizers TensorFlow and Keras PyTorch Fundamentals Model Training and Evaluation

08Computer VisionModule 8 · 1 topics

Introduction to Computer Vision Image Processing Fundamentals Image Classification Object Detection Convolutional Neural Networks (CNN) OpenCV Fundamentals Transfer Learning Practical Image Recognition Applications

09Natural Language Processing (NLP)Module 9 · 1 topics

Introduction to NLP Text Preprocessing Tokenization and Stemming Lemmatization Text Classification Sentiment Analysis Named Entity Recognition (NER) Word Embeddings NLP Model Implementation

10Generative AI & Large Language ModelsModule 10 · 1 topics

Introduction to Generative AI Generative AI Applications Understanding Large Language Models (LLMs) Transformer Architecture Introduction to GPT Models Prompt Engineering Working with OpenAI and Google Gemini APIs AI-Powered Content and Application Development

11Retrieval-Augmented Generation (RAG)Module 11 · 1 topics

Introduction to RAG Architecture Document Loading and Processing Text Chunking and Embeddings Vector Databases Semantic Search Similarity Search Building RAG Applications RAG Evaluation and Optimization

12AI Application Development & DeploymentModule 12 · 1 topics

Building AI Applications using Python Introduction to Streamlit and FastAPI Integrating AI Models through APIs Model Deployment Introduction to Docker Cloud Deployment Fundamentals AI Application Testing and Monitoring Responsible AI and Model Security

13Practical Projects & Career PreparationModule 13 · 1 topics

Hands-on AI Development Labs Machine Learning Projects Deep Learning Applications Generative AI Application Development RAG-Based AI Application Real-World AI Engineering Projects Git and GitHub Portfolio Resume Building and Interview Preparation

Find out how this curriculum fits your career goals.

Tools & technologies

Explore industry-relevant tools through hands-on learning to master practical, in-demand skills.

Python

NumPy

Pandas

Matplotlib

Scikit-learn

TensorFlow

Keras

PyTorch

OpenCV

Hugging Face

LangChain

OpenAI API

Google Gemini API

ChromaDB

Streamlit

FastAPI

Build practical skills with industry-relevant learning, expert guidance, and hands-on projects designed for modern teams.

Get the complete curriculum, tools, projects, and career support details in one brochure.

Build something that matters

6 Industry Projects

Every student completes 6 real-world projects — one per month — building a professional portfolio demonstrated during placement interviews.

Project 01

AI-Based Student Performance Prediction System

Collect and preprocess student academic data. Perform exploratory data analysis using Python. Apply Machine Learning algorithms such as Decision Tree and Random Forest. Predict student performance based on attendance, marks, and study patterns. Evaluate model accuracy and generate prediction reports.

Project 02

AI-Powered Image Classification System

Collect and prepare an image dataset. Perform image preprocessing and augmentation. Build a Convolutional Neural Network (CNN). Train a Deep Learning model to classify images. Test model performance on new images.

Project 03

AI-Powered Customer Support Chatbot

Develop an AI chatbot using a Large Language Model (LLM). Integrate OpenAI or Google Gemini API. Design prompts for customer queries. Build a chatbot interface for interactive conversations. Test responses and improve chatbot performance.

Project 04

Document-Based AI Assistant Using RAG

Upload and process PDF documents. Extract text and create document embeddings. Store embeddings in a vector database. Implement Retrieval-Augmented Generation (RAG). Build an AI assistant that answers questions based on uploaded documents.

Portfolio tip: document the problem, your approach, and the result so you can walk an interviewer through your decisions.

Learning process from career advice and enrolment through practical sessions, projects, interview preparation, and placement

Who should enroll

Developers moving into AI
Engineering graduates
Data professionals adding GenAI skills
Working professionals building AI apps

Learning modes

Classroom training at Thane center
Live online interactive sessions
Recorded sessions for missed classes
Free demo class before enrollment

Prerequisites

Basic Python or programming knowledge is helpful
Logical thinking and problem-solving aptitude
Own laptop for lab work

95% of our students successfully get placed after completing our programs.

Learn more about how we have been impacting thousands of careers.

Apply AI Engineering skills to practical business scenarios
Use Python, OpenAI APIs, LangChain with confidence
Build project work that can be shown in interviews
Prepare for relevant certifications and job roles

Recognition that travels with you

Globally recognized certifications

AI Engineering Certificate

Certification pathway

Stand out with proof of practical skills.

Build a credible portfolio of skills with structured preparation, practical projects, and certification guidance from experienced mentors.

Beyond the classroom

Career Assistance Program

Soft skill session

Resume building

Aptitude training

LinkedIn profile building

Mock interview

Job assistance

Placement Assistance

Dedicated placement support with industry connections and recruiter interactions.

Dedicated placement cell with job referrals

Resume & LinkedIn profile optimization

Mock interviews with industry experts

Interview call scheduling support

Career counseling until you get placed

Career and placement support for Data Analytics learners

Access your complete learning experience through our all-in-one learning portal.

SkillUpAll-in-one

01Live Mentor Sessions

02AI-Powered Practice

03Progress Tracking

04Career support

One learning hub for your complete career journey

Your next opportunity starts here

Limited seats. High demand. Enroll today.

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Enroll for one course and get access to the complete learning journey
In-depth curriculum with live mentor support
Integrated projects and industry-recognized certifications
Practice on real-world cases and career-ready assignments
Dedicated placement guidance and interview preparation

Career growth through practical learning

CAREER OUTCOME

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Your practical skills, projects, and career preparation can help you approach interviews with clarity and confidence.

Role-ready preparation

Portfolio, interview practice, and mentor feedback.

Career counsellor

READY

A little more clarity

Frequently asked questions

Answers to common questions about learning, projects, and getting started.

What is AI Engineering?

AI Engineering is a field focused on designing, developing, testing, and deploying artificial intelligence systems and applications to solve real-world problems.

What will I learn in the AI Engineering course?

You will learn Python programming, Mathematics for AI, Data Analysis, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), Generative AI, Large Language Models (LLMs), and AI application development.

Who can join the AI Engineering course?

Students, freshers, graduates, software developers, IT professionals, and working professionals interested in building a career in Artificial Intelligence can join this course.

Do I need programming knowledge to learn AI Engineering?

Basic programming knowledge is helpful, but beginners can start with Python fundamentals before moving to advanced AI concepts.

What are the prerequisites for learning AI Engineering?

Basic computer knowledge and an interest in programming are helpful. Knowledge of Python, mathematics, and statistics can make learning easier.

Which programming language is used in AI Engineering?

Python is one of the most widely used programming languages in AI Engineering because of its libraries and frameworks for Machine Learning, Deep Learning, and Generative AI.

Which tools and technologies are covered in AI Engineering training?

The course may cover Python, NumPy, Pandas, Scikit-learn, TensorFlow, Keras, PyTorch, OpenCV, Hugging Face, LangChain, OpenAI API, Google Gemini API, ChromaDB, and Streamlit.

What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?

Artificial Intelligence is the broader field of creating intelligent systems. Machine Learning is a subset of AI that learns patterns from data, while Deep Learning uses multi-layer neural networks to solve complex problems.

Does the AI Engineering course include practical training?

Yes, students gain hands-on experience through coding exercises, machine learning experiments, deep learning models, AI application development, and real-world projects.

What projects will I complete during the AI Engineering course?

Students can work on projects such as AI-Based Student Performance Prediction, Image Classification System, AI-Powered Customer Support Chatbot, and Document-Based AI Assistant using RAG.

What is Generative AI in AI Engineering?

Generative AI is a branch of artificial intelligence that creates new content, including text, images, code, and other outputs using trained AI models.

What are Large Language Models (LLMs)?

Large Language Models are AI models trained on large amounts of text data to understand and generate human-like language. Examples include GPT and Google Gemini models.

What career opportunities are available after completing AI Engineering training?

You can explore roles such as Junior AI Engineer, Machine Learning Engineer, Python AI Developer, Generative AI Developer, and AI Application Developer, depending on your skills and experience

Is AI Engineering a good career choice?

AI Engineering offers opportunities across industries such as IT, healthcare, finance, education, and e-commerce. Practical skills and project experience can help you prepare for emerging AI-related roles

Does Raystech Academy provide placement assistance after AI Engineering training?

Raystech Academy provides career guidance, resume-building support, interview preparation, and placement assistance to help students prepare for relevant job opportunities.

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