Generative AI Training In Hyderabad
with
100% Placement Assistance
- Capstone Projects
- Industry Ready Curriculum
- Start From Foundation Level Training
Learn Generative AI, Prompt Engineering, and real AI tools with hands-on projects and industry placement help in Hyderabad or online. This training is built for beginners, students, and job seekers to get job-ready skills and real world experience
Generative AI Training In Hyderabad
Batch Details -Start Your AI Training in Hyderabad
| Trainer Name | Bharath Sri Ram,sandeep and Dinesh sir |
| Trainer Experience | 10+ Years, 20+ Years |
| Next Batch Date | 16th february 2026 (12:00 PM IST Offline)16th february 2026(08:30 AM Online) |
| Training Modes | Classroom Training (Hyderabad), Online Training |
| Course Duration | 3 Months |
| Call us at | +91 9885044555 |
| Email Us at | genaimasters@gmail.com |
| Demo Class Details | Click Here to Chat on Whatsapp |
Generative AI Training In Hyderabad
Latest Course Curriculum 2026
- introduction of python.
- Assignment of variables.
- comments.
- Primitive Data types
- Type Casting
- python string operations.
- Non-privtive Data types. –(List,Tuples,Set,Dictionaries)
- Python Conditions and If statements
- Python – List Comprehension
- The Python Match Statement
Note:-
1). Hands-on project on Conditional statements.
2)Mock Test1
Modelue-2
- Python Loops statement. (for,while)
- python Functions
- Python Lambda Function.
- Python OOPS Concepts
- Python Modules creation.
- Exceptional Handling.
- Multithreading
- Genrator,Itterator,Map,reduer,filter,apply functions.
Note:–
i).Final Project.
ii).Mock Test2
Goal: Build a strong foundation in Python basics.
- Day 1 (1 hr)
- Introduction to Python
- Python installation, IDEs (Google colab/Jupyter)
- Understanding Machine Learning Fundamentals, Limitations of ML,
- Deep Learning, AI vs ML vs Generative AI.
- Your first Python program (Hello World)
- Input & Output
- Day 2 (1 hr)
- Assignment of variables
- Data types overview (Primitive vs Non-Primitive)
- Constants vs variables
- Day 3 (1 hr)
- Comments (Single-line & Multi-line)
- Primitive Data Types (int, float, bool, str)
- type(), id(), isinstance()
Day 4 (1 hr)
- Type Casting (int(), float(), str(), bool())
- String operations: indexing, slicing, concatenation
- String methods (upper(), lower(), split(), strip())
- Day 5 (1 hr)
- Non-Primitive Data Types overview
- Lists: create, access, modify
- Tuples: immutability, indexing
- Sets: uniqueness, operations (union, intersection)
- Dictionaries: key-value pairs
Goal: Master conditions, loops, and comprehension.
- Day 6 (1 hr)
- Python Conditions (if, elif, else)
- Nested if statement
- Logical operators
- Day 7 (1 hr)
- List Comprehension
- Set & Dict comprehensions
- Practical problems
- Day 8 (1 hr)
- The Python match statement (like switch-case)
- Examples with patterns
- Day 9 (1 hr)
- Loops – for and while
- break, continue, pass
- Loop with else
- Day 10 (1 hr)
- Nested loops
- Iterating over lists, sets, dicts
- Practical exercises
Goal: Learn functions and Object-Oriented Programming.
- Day 11 (1 hr)
- Python Functions: def, arguments, return
- Default & keyword arguments
- Docstrings
Day 12 (1 hr)
- Lambda functions
- map(), filter(), reduce()
- apply() with Pandas intro
- Day 13 (1 hr)
- OOP Concepts: Class & Object
- __init__ method
- Instance vs Class variables
- Day 14 (1 hr)
- OOP: Inheritance
- Polymorphism
- Encapsulation & Abstraction
- Day 15 (1 hr)
- Access Modifiers (public, private, protected)
- Static Methods & Static Variables
- Day 16 (1 hr)
- IS-A vs HAS-A Relationship
- Aggregation vs Composition
- Method Overriding & super keyword
- OOP Best Practices (Clean & Reusable Code)
- Day 17 (1 hr)
- Python Modules creation
- Importing & using built-in modules (math, random, os)
- Creating your own module
- Goal Master python Libraries & practical usage.
• Day 16 (3 hr)
• Numpy
• operations
• Day 17 (3 hr)
• pandas
• Data Frames
• Operations
• Day 18 (1 hr)
• Matplotlib
• Data visualization
• Graphs & chartsDay 19 (1 hr)
• Seaborn
• Statistical visualization Techniques Day 20 (1 hr)
• Final Project
• Simple To-Do App
• Uses functions, loops, conditions, exceptions, and OOP, Modules
Tools: Python
• 1.Statistics.
• A. Statistics, Limitations, Applicatinos statistics.
• B. Data, Types of Data.
• C. Population, samples
• D. Types of samplings
• E. Types of statistics.
• F. Descriptive statistics
• i)Measure of central Tendency.
• ii)Measure of Variability.
• iii)Distribution shape
• iv)Graphical Visualization.
• 7.inferential statistics.
• i)Estmation parametr.
• ii)Regression analyses
• iii)confidance interval
• iv)Hypothesises Test.
• Statistical Tests
• vi)Correlation & Association2.Probability
• A. 1.Trail and outcome
• B. 2.sample Space.
• C. 3.what is Event, Types of Event in probability.
• D. 4.Random variable,Types of Radom varible.
• E. 5.Permitations and combinations
• F. 6.conditional probability.
• G. 7.Bayes’ Theorem
• H. 8.probability Distrilbution
Week 1 – Statistics Fundamentals (5 Hours)
• Day 1 (1 hr)
• Introduction to Statistics
• Limitations of Statistics
• Applications of Statistics in Data Science
• Day 2 (1 hr)
• Data & Types of Data (Qualitative, Quantitative)
• Population vs Sample
• Types of Sampling (Random, Stratified, Cluster, Systematic)
• Day 3 (1 hr)
• Types of Statistics → Descriptive vs Inferential
• Descriptive Statistics Overview
• Day 4 (1 hr)
• Descriptive Statistics
• Measures of Central Tendency (Mean, Median, Mode)
• Measures of Variability (Range, Variance, Std. Dev, IQR)
• Day 5 (1 hr)
• Descriptive Statistics (continued):
• Distribution Shape (Skewness, Kurtosis)
• Graphical Visualization (Histogram, Boxplot, Bar, Pie, Scatterplot)
• Day 6 (1 hr)
• Inferential Statistics Overview
• Estimation of Parameters
• Confidence Intervals (mean & proportion)
• Day 7 (1 hr)
• Hypothesis Testing (Null vs Alternative, Errors, p-value, significance level)
• Statistical Tests: t-test, Chi-square, ANOVA
• Day 8 (1 hr)
• Regression Analysis (Simple Linear, Logistic basics)
• Correlation & Association (Pearson, Spearman, Covariance)
Probability
• Day 9 (1 hr)
• Probability Basics
• Trial & Outcome, Sample Space
• Events & Types of Events (Mutually exclusive, Independent, Complementary)
• Day 10 (1 hr)
• Random Variables (Discrete, Continuous)
• Permutations & Combinations
Day 11 (1 hr)
• Conditional Probability
• Bayes’ Theorem (with real-world example: medical test / spam filter)
- Day 12 (1 hr)
- Probability Distributions
- Discrete (Binomial, Poisson)
- Continuous (Normal, Uniform, Exponential)
• A. Supervised, Unsupervised, and Reinforcement Learning.
• B. Structured Data vs. Unstructured Data.
• C. Confusion Matrix.
• D. Data Cleaning and Preprocessing data for Analysis.
• E. Regression and Classification ML Algorithms.
• F. Model Selection and Cross-Validation.
• G. Feature Engineering.
• H. NLP (Natural Language Processing) and Text Mining.
• I. Text Preprocessing.
-
Day 1 (1 hr)
Introduction + Data Types
• What is ML?
• Types of Learning: Supervised, Unsupervised, Reinforcement Learning.
• Structured vs Unstructured Data.
Day 2 (1 hr)
Confusion Matrix & Evaluation Metrics
• TP, TN, FP, FN.
• Accuracy, Precision, Recall, F1-score.
Day 3 (1 hr)
• Handling missing values.
• Removing duplicates.
• Outlier detection (Z-score, IQR).
Day 4 (1 hr)
• Encoding categorical features (LabelEncoder, OneHotEncoder).
Day 5 (1 hr)
• Feature scaling (StandardScaler, MinMaxScaler).
• Train-test split.
Data Preprocessing II
Data Preprocessing I
Data Cleaning
Day 6 (1 hr)
• Regression Basics
• Linear Regression, Multiple Regression.
• Regularization: Ridge, Lasso, ElasticNet.
• Day 7 (1 hr)
• Classification Basics
• Logistic Regression.
• Naive Bayes.
• k-Nearest Neighbors.
• Day 8 (1 hr)
• Decision Trees
• Splitting criteria: Gini, Entropy.
• Overfitting in trees, pruning.
• Day 9 (1 hr)
• Ensemble Methods
• Bagging: Random Forest.
• Boosting: AdaBoost, Gradient Boosting, XGBoost.
• Day 10 (1 hr)
• Support Vector Machines (SVM)
• Linear & Nonlinear SVM.
• Kernel trick (RBF, Polynomial).
Day 11 (1 hr)
• Feature Engineering
• Creating new features.
• Feature selection (filter, wrapper, embedded methods).• Day 12 (1 hr)
• Dimensionality Reduction
• PCA (Principal Component Analysis).
• t-SNE (intro only).• Day 13 (1 hr)
• Hyperparameter Tuning
• GridSearchCV.
• RandomizedSearchCV.• Day 14 (1 hr)
• Recommendation Systems Basics
• Content-based filtering.
• Collaborative filtering (user-based, item-based).• Day 15 (1 hr)
• Recommendation Systems Advanced
• Matrix Factorization (SVD).
• Surprise library.
Day 16 (1 hr)
• NLP Basics
• What is NLP & Text Mining?
• Real-world applications.• Day 17 (1 hr)
• Text Preprocessing I
• Tokenization.
• Lowercasing, removing punctuation, stopwords.• Day 18 (1 hr)
• Text Preprocessing II
• Lemmatization, stemming.
• Bag of Words, TF-IDF.• Day 19 (1 hr)
• Text Classification
• Naive Bayes, Logistic Regression for text.• Day 20 (1 hr)
• Capstone Project
• End-to-End Project combining numerical + text features.
• Workflow: data cleaning → feature extraction → model → evaluation
A. Artificial Neural Networks (ANNs)
B. Deep Learning
C. Convolutional Neural Networks (CNNs)
D. Recurrent Neural Networks (RNNs)
E. Long Short-Term Memory (LSTM)
F. Generative Adversarial Networks (GANs)
G. Transformer Architecture.
Day 1 (1 hr) Artificial Neural Networks (ANNs)
• Introduction to Neural Networks.
• Neuron structure: inputs, weights, bias, activation.
• Common activation functions: Sigmoid, Tanh, ReLU, Softmax.
• Use cases of ANNs in decision sciences.
Day 2 (1 hr): Deep Learning Foundations
• Shallow vs Deep networks.
• Training process: Forward pass, Backpropagation, Loss functions.
• Gradient Descent & Optimizers (SGD, Adam, RMSProp).
Day 3 (4 hrs): Convolutional Neural Networks (CNNs)
• CNN architecture: Convolution, Pooling, Fully Connected layers.
• Image feature extraction & hierarchical learning.
• Popular CNN models: LeNet, AlexNet, VGG, ResNet.
• Transfer Learning with CNNs.
Day 4 (4 hrs): Recurrent Neural Networks (RNNs) & LSTMs/GRUs
• RNN architecture & sequence modeling.
• Challenges: Vanishing/exploding gradients.
• Long Short-Term Memory (LSTM): gates & memory cells.
• Gated Recurrent Units (GRU) as a simpler alternative.
Day 5 (2 hrs): Advanced Generative & Attention-Based Models
• Generative Adversarial Networks (GANs): Generator vs Discriminator, applications in synthetic data & images.
• Variational Autoencoders (VAEs): Encoding, decoding, latent representations.
• Transformers: Attention mechanism, encoder-decoder structure.
Pretrained Large Models: BERT, GPT, Vision Transformers.
Week 1: Generative AI & LLM Foundations (5 hrs)
Day 1: Introduction to Generative AI
- What is Generative AI?
- Applications (Text, Image, Audio, Video, Code).
Day 2: Introduction to Large Language Models (LLMs)
- What are LLMs?
- Training concepts (pre-training, fine-tuning, RLHF).
Day 3: Word Embeddings
- Concepts of embeddings.
- Word2Vec, GloVe, contextual embeddings.
Day 4: Hugging Face Basics
- Introduction to Hugging Face Hub.
- Exploring pre-trained models.
Day 5: Hugging Face Pipelines
Using pipeline for NLP tasks (sentiment, summarization, Q&A).
Day 6: Hugging Face Without Pipelines
- Loading models/tokenizers manually.
- Inference with Transformers.
Day 7: Fine-Tuning Models on Hugging Face
- Dataset preparation.
- Training & evaluation basics.
Day 8: Fine-Tuning Hands-On (Text Classification)
- Train a classifier using Hugging Face Trainer API.
Day 9: Hugging Face Advanced Use
- Model saving, deployment options.
Day 10: Project Discussion
- Choosing use cases for fine-tuned models.
Day 11: Introduction to LangChain
• What is LangChain?
• Why it’s important for GenAI apps.
• Day 12: LangChain Core Components
• Prompt Template, LLM, Output Parsers.
• Day 13: LangChain Chains
• Sequential chains.
• Input/Output design.
• Day 14: LangChain Memory
• Conversation Buffer, Conversation Summary.
• Memory in chatbots.
• Day 15: LangChain Agents
• Tools & Agents.
• When to use agents.
• Day 16: LangGraph
• What is LangGraph?
• Why LangGraph is needed beyond LangChain
• Graph-based control flow for LLMs
• Nodes, edges, and state
• Deterministic vs dynamic execution
• LangChain vs LangGraph comparison
• Day 17: Lang Graph Core Concepts
• State management in LangGraph
• Nodes as functional units
• Conditional edges and branching logic
• Cycles and looping behavior
• Human-in-the-loop workflows
Day 16: RAG Introduction
• What is RAG?
• Architecture & workflow.
Day 17: Vector Databases
• Embeddings storage.
• Using FAISS/Chroma.
Day 18: RAG Implementation
• Document loaders, splitters, embeddings.
• Building a retriever.
Day 19: RAG Q&A Chain
• Connecting retriever + LLM.
• Testing with real datasets.
Day 20: Validating RAG Performance
• Evaluating precision/recall in RAG.
Day 21: Mastering Chatbots with Memory
- Multi-turn conversations.
- Long-term memory.
Day 22: LangChain Advanced Agents
- Multi-tool agents.
- Planning vs Reactive agents.
-
Day 1: Introduction to Agentic AI
• What is Agentic AI?
• Evolution of AI (Rule-based → ML → LLMs → Agentic AI)
• LLMs vs Chatbots vs AI Agents
Day 2: AI Agent Architecture
• What is an AI Agent?
• Core components of an Agentic AI system
• Agent lifecycle (Think → Plan → Act → Observe → Iterate)
Day 3: Agent Reasoning & Planning
• Reasoning in Agentic AI
• Chain-of-Thought & ReAct pattern
• Planning-based agents vs Reactive agents
Day 4: Agentic AI Framework – LangChain Agents
• Why frameworks are needed for Agentic AI
• LangChain Agents overview
• Chains vs Agents vs RAG
Day 5: Agentic AI Applications & Design
• Real-world Agentic AI use cases
• multi-agent systems (conceptual)
• Designing an end-to-end Agentic AI workflow
Day 6: Introduction to MCP (1 hrs)
• What is MCP (Model Context Protocol)?
• Why MCP is needed in modern GenAI systems
• Limitations of tool calling without MCP
• MCP as a standardized interface between:
LLMs
Tools
Data sources
• MCP vs traditional API integrations
• Role of MCP in scalable Agentic AI
Day 7: MCP Architecture & Core Concepts (1 hrs)
MCP architecture overview
• MCP servers and MCP clients
• Resources, tools, and prompts in MCP
• Context management and state sharing
• Security and access control concepts
• How MCP enables reusable AI tools
Day 1: Advanced Prompt Engineering (Part 1)
- Principles of effective prompting.
- Zero-shot, Few-shot, Chain-of-thought prompting.
Day 2: Advanced Prompt Engineering (Part 2)
- Structured outputs (JSON, tables).
- Prompt optimization & evaluation techniques.
Trainer Details - Generative AI Training in Hyderabad
Ms. Madhumathi
Principal Data Scientist & Generative AI Strategist
10+ Years of Experience
About the Tutor
As an industry expert in Generative AI and an accomplished lead trainer, she is dedicated to advancing students’ careers by integrating real-time applications into Data Science and Generative AI education.
she is passionate about helping students build a successful career in Generative AI and Data Science. She also introduces learners to tools like Python, TensorFlow, OpenAI APIs, and Hugging Face, helping them understand how to apply AI in business, marketing, and technology.
With her friendly teaching style and real-time examples, she ensures every student gains both technical knowledge and practical confidence to work on AI-powered applications in the real world.
Mr. Dinesh
Generative AI Authority & Principal Data Scientist
20+ years of Experience
About the Tutor
Our trainer is an Expert in AI specialist and Lead Data Scientist with deep expertise in Machine Learning, Deep Learning, NLP, Python/R, along with Statistical, Biological, and Panel Analysis.
The trainer has extensive experience in the healthcare and medical fields, having managed projects across the US, UK, Australia, and Canada. He possesses strong expertise in image processing for various diseases, utilizing Generative AI techniques.
He also guides students on how to use AI tools, frameworks, and APIs such as TensorFlow, PyTorch, OpenAI, and Hugging Face, enabling them to design and deploy real-world AI solutions. As a passionate mentor, he focuses on career growth, helping students build strong analytical thinking, problem-solving skills, and AI project experience to achieve success in the global Data Science and AI industry.
Why Choose us for Generative AI Training
Expert Faculty
Generative AI Masters has a team of experienced instructors who are industry experts in Generative AI. Their deep knowledge and practical experience ensure high-quality training.
Complete Curriculum
The training program covers a broad range of topics, including foundational concepts, advanced techniques, and real-time applications of Generative AI, providing a thorough understanding of the field.
Hands-On Learning
The course includes practical sessions and projects that allow students to apply theoretical knowledge to real-time problems, enhancing their learning experience and skill development.
Industry-Relevant Projects
Students get the opportunity to work on industry-relevant projects that reflect current trends and challenges in Generative AI, preparing them for real-time scenarios.
State-of-the-Art Facilities
The institute is equipped with advanced technology and software necessary for Generative AI training, ensuring that students have access to the latest tools and resources.
Personalized Support
We at Generative AI Masters provides individualized attention and mentorship to help students with their unique learning needs and career goals, with a supportive learning environment.
Career Assistance
The institute offers career guidance, including resume building, interview preparation, and job placement assistance, to help students transition smoothly into the industry.
Networking Opportunities
Students have access to a network of professionals, industry leaders, and alumni, providing valuable connections and opportunities for collaboration and growth.
Flexible Learning Options
We Generative AI Masters offers various learning formats, including online, in-person, and hybrid options, helping to different learning preferences and schedules.
Strong Reputation
The institute has a proven track record of success, with positive reviews from past students and praise from industry professionals, reflecting its credibility and effectiveness in Generative AI training. in Hyderabad.
Modes - Generative AI Course in Hyderabad
Classroom Training
- Interactive Face-to-Face Teaching
- Industry Expert Trainers
- Instant Feedback
- Collaborative Tasks
- Hands-on Industry Projects
- Group Discussions
- Covers Advanced Topics
Online Training
- Virtual Learning Sessions
- Daily Session Recordings
- Instructor Support
- Interactive Webinars
- Digital Learning Modules
- Online Practical Labs
- Flexible Learning Schedules
Corporate Training
- Customized Training Programs
- Daily Recordings
- Interactive Team Development
- Expert Instruction
- Industry-Relevant Content
- Performance Monitoring
- On-Site Workshops
What is Generative AI
- Generative AI is a type of artificial intelligence that creates new content, such as text, images, or music.
- It uses advanced algorithms and neural networks to generate outputs that are not just copies of existing data.
- Generative AI models learn patterns and structures in data to produce unique and realistic results.
- Popular types of generative AI include Generative Adversarial Networks (GANs) and Transformers.
- It can be used in various fields, from content creation and art to scientific research and simulations.
- Generative AI is capable of generating creative outputs that mimic human-like creativity.
- It is widely used in industries like entertainment, design, and technology for tasks such as image synthesis and text generation.
- As it grows, Generative AI is raising important discussions about ethics, originality, and the implications of AI-generated content.
- If you are looking to learn Generative AI from industry expert trainers then Generative AI Masters is the Best option
If you want to learn more about Generative AI Syllabus
Tools Covered as part of Generative AI Training
In Generative AI training, participants usually learn to use different tools and software that are important for building and handling generative AI models. These tools include
An open-source machine learning framework widely used for building and training neural networks.
Hugging Face Transformers
OpenAI GPT
Jupyter Notebooks
NVIDIA CUDA
Google Colab
Docker
Skills Develop After Generative AI Training
- Skilled in designing and implementing advanced Generative AI models for a wide range of applications, including content creation, automation, data analysis, and intelligent decision-making systems.
- Skilled in creating high-quality and realistic content such as text, images, and audio using advanced AI techniques and generative models, ensuring accuracy, creativity, and human-like results across various applications.
- Strong understanding of core concepts in Machine Learning and Deep Learning that form the foundation of Generative AI, including neural networks, training algorithms, and model optimization techniques.
- Knowledge of different generative models like GANs and Transformers and their practical uses.
- Experienced in applying Generative AI solutions to real-world projects and case studies, demonstrating the ability to solve practical problems, enhance automation, and create innovative AI-driven applications.
- Skills in evaluating and fine-tuning AI models to improve performance and accuracy.
- Familiarity with ethical considerations and best practices in deploying generative AI technologies.
- Competence in using AI tools and software to develop and manage generative AI projects effectively.
Career Opportunities After Generative AI Training in Hyderabad (2026)
After completing our Generative AI Training in Hyderabad, you will be ready for high-demand AI jobs in tech, startups, and digital companies. In 2026, companies don’t just look at certificates — they look at real skills, projects, and tools you can use in real work.
Job Roles You Can Target
1. Generative AI Engineer
You will build AI systems that create text, images, code, and automation tools using LLMs like GPT, Gemini, and Claude.
2. Prompt Engineer / AI Specialist
You will design smart prompts and workflows so AI gives accurate, useful, and business-ready outputs.
3.AI Research Scientist – Conducts research to develop new generative models and improve existing ones.
4.Machine Learning Engineer – Designs and builds machine learning models, including generative AI systems, for various applications.
5.Data Scientist – Analyzes and interprets complex data to develop generative AI solutions and derive actionable insights.
6.NLP / AI Application Developer– Creates and implements AI solutions and applications using generative models for tasks like text and image generation.
7.Computer Vision Engineer – Focuses on developing generative models for image and video synthesis and analysis.
8.Natural Language Processing (NLP) Engineer – Works on generative models for text generation, language understanding, and conversational AI.
9.AI Product / Automation Associate – Oversees the development and deployment of AI products, including those using generative technologies.
10.AI Consultant – Advises businesses on implementing generative AI solutions to solve specific problems and enhance processes.
11.Ethics and Compliance Specialist – Ensures that generative AI applications are developed and used in an ethical and compliant manner.
12.AI Trainer/Instructor – Provides training and education on generative AI technologies to individuals and organizations.
Generative AI Salaries in Hyderabad – 2026 (Expected)
Generative AI is growing fast in Hyderabad, especially in IT companies, startups, research labs, and product-based companies. By 2026, the demand for AI skills like LLM development, prompt engineering, machine learning, deep learning, and AI engineering will be even higher.
| Job Role | Experience Level | Skills Required | Expected Salary (2026) | Who Hires in Hyderabad |
|---|---|---|---|---|
| AI Engineer | 0–3 years | Python, ML, GenAI APIs, NLP | ₹8–18 LPA | TCS, Infosys, Wipro, Amazon, Google |
| Machine Learning Engineer | 1–5 years | ML models, Python, TensorFlow, PyTorch | ₹10–22 LPA | Microsoft, Accenture, Deloitte |
| Generative AI Developer | 1–6 years | LLMs, GPT, fine-tuning, RAG, vector DBs | ₹12–28 LPA | Startups, Product companies |
| Prompt Engineer | 0–4 years | Prompt design, LLM understanding, NLP | ₹8–20 LPA | AI startups, Marketing tech companies |
| Data Scientist (AI + ML) | 2–7 years | Statistics, ML, Deep Learning | ₹12–30 LPA | BFSI, Healthcare, Enterprise Companies |
| AI Research Scientist | 3–10 years | Research papers, LLM training, deep learning | ₹20–50 LPA | Research labs, MNCs, Universities |
| NLP Engineer | 1–6 years | NLP models, Transformers, embeddings | ₹10–25 LPA | Product companies, SaaS firms |
| AI Product Manager | 4–12 years | AI project mgmt, product strategy | ₹25–60 LPA | Product-based companies |
| MLOps Engineer | 2–7 years | CI/CD for ML, cloud, Docker, Kubernetes | ₹12–26 LPA | Cloud companies, AI platforms |
| AI Consultant | 5+ years | Strategy, AI integration, client solutions | ₹30–70 LPA | Big 4, IT Consulting Firms |
Why Generative AI Salaries Are Increasing in Hyderabad
More companies are shifting to AI automation and AI-powered products
High demand for LLM developers & prompt engineers
Hyderabad is becoming a major hub for AI research and innovation
Global companies are opening AI labs in the city
Skilled Generative AI professionals are limited, so salaries increase
Companies That Hire
Generative AI Placement Program
Generative AI Masters Institute offers a Complete placement program designed to support students of its Generative AI training in securing rewarding job opportunities. The program includes personalized career counseling, resume building, and interview preparation to help candidates effectively showcase their skills to potential employers.
The placement program also includes job fairs, recruitment drives, and networking events where students can meet real recruiters and industry professionals. These events give learners a chance to talk directly with hiring managers, understand what companies are looking for, and explore real job opportunities in Generative AI.
We focus strongly on matching each student’s career goals with current market needs. This means we guide you to choose the right role based on your skills, interests, and industry demand.
- To make sure every graduate is confident, skilled, and ready to enter the competitive field of Generative AI — and to grow into higher roles as their career advances.
Job-Ready Training with Real Career Support (2026)
- Intensive 3-Month Curriculum
Engage in a structured, hands-on training program with weekly assignments, real AI tools, and practical tasks designed to build strong Generative AI expertise. - Complete Project Execution
Gain real-world experience by building end-to-end AI applications such as chatbots, document AI systems, and automation tools used in actual companies. - Multiple Capstone Projects
Work on industry-style capstone projects that show your skills in LLMs, prompt engineering, RAG systems, and AI deployment. - Practical Work Experience
Get hands-on exposure that prepares you for real job roles, team workflows, and problem-solving in a professional AI environment. - Interview & Hiring Preparation
Receive expert guidance on technical interviews, AI case studies, prompt challenges, and HR rounds with mock sessions and feedback. - Simulated Work Environment
Train in a real-time, company-style environment with deadlines, reviews, collaboration, and performance tracking. - Portfolio & Resume Building
Create a strong AI portfolio with live project links, GitHub work, and a job-ready resume that attracts recruiters. - Placement Assistance & Career Support
Get continuous support with job applications, referrals, and guidance to secure a role in Generative AI and AI engineering fields. - Hyderabad Job Market Focus
Learn with placement strategies aligned to Hyderabad’s AI and tech ecosystem, including startups and IT companies.
With this Generative AI Placement Program, you don’t just learn AI — you become job-ready, confident, and competitive in the 2026 AI job market.
Difference between Traditional Training and Generative AI Masters
- Traditional Training
- Generative AI Masters Training
just basics with only theory based training
- Hardly any practical Exposure with industry requirements.
Advanced practical training
- You get practical knowledge of the subject with hands-on experience as per industry requirements
Zero Job assurance
- You only receive training, after that it's up to you to find Job.
100% placement assistance
- At Generative AI Training in Hyderabad, we provide full support to help you find work, from scheduling interviews to boosting your employability.
Basic curriculum
- You are trained with generalized basic concepts.
Industry Ready Curriculum
- We follow a curated and industry demanding curriculum with advanced topics designed by the industry experts.
Huge upfront course fee
- You are charged very high course fees for training and course materials alone.
Flexible Payment Options
- We offer a small initial training fee, with the remainder payable in term installments.
Very Limited corporate tie ups
- You miss out on good hiring opportunities owing to the lack of industrial ties.
Top partnered companies
- Start your career by getting hired by the top companies in the industry. Career Development Oriented program:
Unstructured training programme
- The entire course is taught in a matter of weeks, and it is done so quickly.
Systematic training program
- We offer an extensive 3 month training program with online & offline assistance.
Generative AI Training In Hyderabad
5000+ jobs Opening for Generative Ai
Pre-requisites to attend Generative AI Course Online
- A basic understanding of programming, especially Python, is important because it is the main language used to build and run AI models. You should know how to write simple programs, use functions, and work with libraries so you can develop and test Generative AI systems properly.
- Having basic knowledge of machine learning and statistics will help you understand how AI models learn from data. This includes ideas like training models, making predictions, and evaluating results. These concepts make it easier to learn advanced Generative AI topics.
- Understanding linear algebra and calculus is useful because they form the mathematical foundation of AI and deep learning models. Topics like vectors, matrices, derivatives, and optimization help explain how models process data and improve their performance.
- Prior experience with data manipulation and analysis tools will help you work with real-world datasets more effectively. Knowing how to clean data, explore it, and prepare it for training models makes it easier to build accurate and reliable AI applications.
Generative AI Job & Market Trends (2025-2026)
Generative AI is growing very fast because it can create realistic text, images, videos, audio, and code. In 2025–2026, businesses use AI not just to assist work, but to produce real content and solutions.
• More industries are adopting Generative AI, especially in entertainment, design, marketing, education, and IT. Companies use AI to personalize content, automate campaigns, and improve user experiences.
• Advanced models like GANs, Transformers, and Diffusion models are driving innovation. These models power tools like ChatGPT, image generators, video creators, and voice assistants.
• Healthcare is a major growth area for Generative AI. AI is used for drug discovery, medical simulations, report generation, and patient interaction tools.
• Companies are investing heavily in Generative AI to automate creative tasks and increase efficiency. AI helps reduce manual work and improve speed and accuracy.
Generative AI tools are becoming easier to use. Today, even non-technical users can build AI workflows using simple platforms, no heavy coding required.
Ethics and responsible AI are now important topics. As AI creates more content, companies focus on safety, bias control, copyright, and data privacy.
The Generative AI market is expanding rapidly in both consumer and enterprise applications, including chatbots, AI content tools, business automation, and product development.
New job roles are being created because of Generative AI, such as
– Generative AI Engineer
– Prompt Engineer
– AI Automation Specialist
– ML Engineer (GenAI focus)• There is strong demand for professionals with Generative AI skills. Companies need people who can design, implement, monitor, and improve AI systems.
• Prompt Engineering is a key skill in 2025–2026. It means writing smart instructions so AI gives accurate, relevant, and useful outputs. It is used in content, coding, automation, and data analysis workflows.
If you learn Generative AI tools + Prompt Engineering + real projects, you become valuable in the job market. This is why Generative AI Training in Hyderabad is in high demand right now.
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Highlights Of Generative AI Course
- The Generative AI course by Generative AI Masters is designed to provide a complete understanding of generative AI technologies.
- The course covers key topics like machine learning, deep learning, natural language processing, and AI ethics.
- Students will learn how to build and deploy generative AI models for various applications, including text, image, and audio generation.
- The curriculum includes hands-on projects and real-world case studies to enhance practical skills.
- We at Generative AI Masters has the most experienced instructors who are industry experts with extensive knowledge in Generative AI.
- The course is suitable for beginners as well as professionals looking to upskill in generative AI.
- Online and flexible learning options are available to accommodate different learning schedules.
- Students will gain access to exclusive AI tools and resources provided by us at Generative AI Masters.
- The institute provides career support and networking opportunities to help students advance in the AI field.
- Upon completion, students receive a certification from Generative AI Masters, recognized by leading tech companies.
- Covers the important modules like MLOPS that can help you in the real time work environment while executing a project.
Generative AI Masters Course Outline
This course is designed for students, freshers, job seekers, people with career gaps, and non-IT professionals who want to build a strong career in Generative AI. We start from the basics and slowly move to advanced, real-world AI skills used in companies today.
01
You will begin by learning what Generative AI is and how it is different from traditional AI. We explain how AI can create text, images, videos, audio, and code. You will see how Generative AI is used in marketing, healthcare, education, finance, and IT companies in 2025–2026.
02
You will learn the basic ideas behind how AI learns from data. This includes training models, understanding patterns, and making predictions. These concepts help you understand how Generative AI systems actually work behind the scenes.
03
You will study different types of generative models such as GANs, Transformers, and Diffusion models. These are the technologies behind tools like ChatGPT, image generators, and voice AI systems.
04
You will get practical experience in building and fine-tuning Generative AI models. This includes working with pre-trained models and improving them for specific tasks like content generation, chatbot creation, and document analysis.
05
The course includes practical projects focused on generating text, images, and audio.
06
You will explore real-time case studies from different industries. You will learn how companies use Generative AI to solve real business problems and improve productivity.
07
You will learn about AI safety, bias, data privacy, and legal issues. We teach best practices for using Generative AI responsibly and professionally in the workplace.
08
You will learn how to deploy AI models and connect them with real applications. This includes working with APIs, cloud platforms, and automation tools used in companies.
Certification
Here are some certifications for Generative AI
- Google Cloud Professional Data Engineer – Includes coverage of machine learning and AI technologies, including generative models.
- Deep Learning Specialization by Coursera – Offered by Andrew Ng, this specialization covers key aspects of deep learning, including generative models.
- Machine Learning and AI Foundations by Microsoft – Provides foundational knowledge in AI, including applications of generative AI.
- Generative Adversarial Networks (GANs) Specialization by Udacity – Focuses specifically on GANs, a major technology in generative AI.
- M AI Engineering Professional Certificate – Covers various AI technologies and includes aspects of generative AI in its curriculum.
- NVIDIA Deep Learning Institute Certifications – Offers certifications in deep learning and AI, including generative techniques.
- Stanford University’s Machine Learning Certificate – Includes coursework on machine learning techniques relevant to generative models.
If you want to learn more about Generative AI Certifications
Faqs
Generative AI is a special kind of Artificial Intelligence that can create new things like text, images, videos, music, and even computer code.
It learns from existing data and uses that knowledge to generate new, meaningful content.
Example tools: ChatGPT, DALL·E, Midjourney, Gemini.
Basic programming skills, familiarity with machine learning concepts, and knowledge of linear algebra and calculus are recommended for the training.
The training covers fundamental concepts of generative AI, deep learning models, GANs, Transformers, practical applications, and ethical considerations.
Freshers: ₹6–10 LPA
1–3 years: ₹10–18 LPA
Senior: ₹20–40+ LPA
Salaries grow fast in AI careers based on your skill and projects.
Generative AI can be used in most industries, including the following:
Entertainment: The creation of scripts, music, and animations.
Healthcare: Produce patient simulations and combine medical images.
Finance: Fraud detection and report automation.
Marketing: Customised advertising and content.
Live online classes (with recording access)
Offline classroom training (Hyderabad)
Interactive doubt-clearing sessions
Trainer-guided projects
Free demo session before joining
Tools such as TensorFlow, PyTorch, Keras, Hugging Face Transformers, and Google Colab are used during the training.
Yes, all applicants and students interested in our Generative AI training program are welcome to attend free trial classes for up to three days.
The duration varies depending on the mode of training, typically ranging from a few weeks to several months.
Yes. You’ll receive an industry-approved certificate from Generative AI Masters after completing the course and projects.
Yes. We provide 100% placement assistance
Resume support
Mock interviews
Real job updates
LinkedIn and portfolio building
Yes! You’ll build 100% real projects like
AI Chatbots
Resume builders
Text-to-image tools
Content generators
YouTube script writers
These help build your portfolio.
You can enroll by contacting us on the Gen AI Masters website, choosing your preferred mode of training, and following the registration process provided.
It saves time and effort by automating content creation.
It improves creativity, marketing, and customer support.
It helps businesses innovate faster.
Generative AI is the future of digital technology.
Anyone
Students
Freshers
Working professionals
Career gap people
Non-tech background
Degree/B.Tech/Inter students
No coding experience is required.
Writing emails, blogs, and product descriptions
Creating logos, banners, posters
Developing AI chatbots
Making YouTube scripts
Resume and job application assistance
AI video creation and editing
Prompt Engineering is the art of giving correct inputs (called “prompts”) to AI tools like ChatGPT to get the best output.
Example
Bad prompt: “Write about sales.
Good prompt: “Write a friendly sales pitch for a beauty product for young women.”
ChatGPT (OpenAI)
Gemini (Google)
Midjourney (Images)
DALL·E 3
Claude (Anthropic)
RunwayML (Video AI)
Notion AI (Content)
Leonardo AI
GitHub Copilot (Code)
We offer full career guidance
Lifetime access to updates
One-on-one doubt solving
Interview questions & mock tests
Placement referrals
- 100% Yes
Many companies now hire freshers trained in Gen AI tools for content, design, and automation tasks.
Yes. Companies today value skills over experience.
If you show your project work, prompt knowledge, and tools, you can re-enter the job market confidently
Yes! The course is made for everyone.
We teach in very simple language with daily life examples.
Even 12th class students can understand it.
We should learn Generative AI because it is the future of technology.
It helps us create new ideas, images, videos, and content using machines.
Learning it can improve our creativity, problem-solving, and job opportunities in many fields like marketing, design, education, and IT.
No, Generative AI will not completely replace IT jobs.
It will only change the way we work.
AI will handle repetitive tasks, but humans are still needed for creative thinking, decision-making, and project management.
So, learning AI will help you work with technology, not be replaced by it.
No, it will not be difficult for everyone to get jobs, but job types will change.
Generative AI will do some simple and repeated tasks automatically.
So, people who learn new skills like AI, data analysis, and creativity will have more job opportunities.
The future will have different jobs, not fewer jobs.
- After completing the course, you can apply for roles like
- Generative AI Engineer
- Prompt Engineer
- AI / ML Engineer
- AI Automation Specialist
- Data Scientist (AI focus)
- NLP / Chatbot Developer
Yes. The curriculum includes latest tools, models, and trends like LLMs, RAG systems, AI automation, and deployment workflows used in today’s companies.
We focus on practical learning, real projects, and career outcomes, not just theory. You don’t just learn — you build, deploy, and prepare for real jobs.
