- NEXT BATCH: 20 MAY 2026
Stop Watching Your Competitors Deploy AI. Start Building It.
The most rigorous Generative AI Training in New York City taught by a practitioner who shipped real enterprise AI inside Amazon, Infosys, and LogiGen. 90 days. Python to production LLM deployment. Live online and onsite NYC delivery.
- Trainers with 10+ Years of Industry Experience
- Hands-On Real-Time AI Projects
- 90-Day Generative AI Training Program
- Online and Classroom Training Available
DURATION
90 Days
TIMINGS
Morning & Evening
TRAINING
Online & Classroom
Get a Free Career Consultation
Our experts will help you choose the right path for your career growth.
AT A GLANCE
- VKNOWTECH AI BY THE NUMBERS
30,000+
Students Trained
15+
Countries
200+
Hiring Partners
99%
Completion Rate
Who Is This Program Actually Built For?
This is not a three-hour ChatGPT awareness seminar. It is not a corporate lunch-and-learn with slides about the future of work. If you came here looking for that, 40 other programs in NYC will happily take $500 from you and hand you a PDF.
This program is built for professionals who need to produce real AI output within their company or career. Building requires learning the full stack from the ground up. That is what happens here.
Profile 1: The Developer Who Needs to Ship AI Features Now
You write code. Your sprint backlog now has tickets that say integrate LLM and build RAG pipeline and you have never touched LangChain or vector databases in a real deployment. This program moves you from syntax to system architecture in 90 days, with four shipped projects as evidence.
Profile 2: The Corporate L&D Manager With a 60-Day Mandate
Your CEO read a Bloomberg report on AI productivity gains at a competitor. You now have a budget, a deadline, and a team of 8 to 25 people who need structured generative AI training. VKNOWTECH AI delivers custom corporate cohorts with role-specific curriculum. Your team ships an internal AI tool before the program ends.
Profile 3: The Professional Making a Deliberate Career Pivot
You are in finance, marketing, product management, or data analysis. AI engineering roles on LinkedIn grew from 12,000 to over 80,000 open positions in 18 months. You need a certification that demonstrates hands-on capability, not a certificate for watching videos. This is that program.
AM I THE RIGHT FIT?
Chat With an Advisor
- admin@vknowtech.ai
Generative AI Portfolio Projects Built in 90 Days
Every module in this curriculum ends with working code. By Day 90 you have four production-
grade portfolio projects on GitHub that you built from scratch, not watched someone else build.
- PROJECT 1
AI Content Generation System
Using the OpenAI API and Python, you build an end-to-end content pipeline that generates articles, product descriptions, and marketing copy at scale. Corporate graduates who deploy this project report cutting content production time by an average of 14 hours per week across their teams.
- PROJECT 2
AI Chatbot with Memory and Persona
Using LangChain and a vector database, you build a fully functional conversational AI assistant that retains context across sessions, handles fallback states, and runs inside a real deployment environment. This is the architecture NYC fintech and media companies are paying $90,000 plus to build right now.
- PROJECT 3
AI Automation Workflow
Using Zapier AI and AutoGPT frameworks, you map and automate a real business process. Graduates who implement this project inside their organizations report eliminating between 10 and 16 hours of manual work per week per team member. That is a quantifiable ROI figure you can take to any CFO.
- PROJECT 4
RAG-Powered Knowledge Assistant
You build a Retrieval Augmented Generation application that turns a document library into a conversational AI search tool. RAG architecture appears in over 60% of senior AI engineer job postings in NYC in Q1 2026. It is the most in-demand LLM engineering skill in the current market.
VKNOWTECH AI vs. NYC Generative AI
Bootcamps
You deserve a straight comparison. Here it is.
Typing into ChatGPT is not an AI strategy. Knowing how to adjust a temperature parameter is not engineering. Programs priced at $3,500 to $16,450 in New York City teach tool usage. VKNOWTECH AI teaches architecture.
If your IDE is not open by Week 2, you are in the wrong program. In this curriculum, you write Python functions in Week 1 and read transformer architecture papers by Week 8. That is the difference between knowing AI and building it.
Full 90-Day Curriculum:
18 Modules, Zero Filler
The curriculum is sequenced deliberately. Every module builds the knowledge required for
the next one. There is no optional content because every section is load-bearing.
Module 1: Getting Started with Python
- Understanding Programming and Coding Basics
- Overview of Python Libraries, Modules, and Web Frameworks
- Exploring Different Flavors of Python
- What Makes Python Powerful? (Use Cases and Advantages)
- Comparing Python’s Syntax with Other Programming Languages
- Setting Up the Python Environment and IDE Installation
Module 2: Python Core Fundamentals
- Utilizing the print Statement and Code Comments
- Deep Dive into Python Data Types and Structures
- Keyword Fundamentals and Variable Declaration
- Type Casting and Conversions
- Handling Standard Input and Output
- Python Operators:
- Arithmetic and Assignment Operators
- Comparison and Logical Operators
- Identity and Membership Operators
- Advanced Output Formatting
Module 3: Control Flow and Logic
- Conditional Statements:
- Understanding Python Indentation Rules
- if, elif, and else constructs
- Nested Conditionals and Shorthand Syntax
- Real-world Conditional Examples
- Iterative Statements (Loops):
- The for Loop and while Loop Mechanics
- Nested Looping Structures
- Practical Examples of Iteration
- Jump Statements:
- Managing Loop Control with break, continue, and pass
Module 4: Advanced Python Data Structures
- String Manipulation: Object Basics, Splitting/Joining, Formatting, and Built-in Methods
- Mastering Lists: Core Concepts, List Methods, Implementing Stacks & Queues, and List Comprehensions
- Understanding Tuples: Creation, Built-in Functions, and Tuple Operations
- Working with Sets: Set Basics, Mathematical Set Operations, and Relevant Functions
- Dictionaries: Key-Value Mapping, Dictionary Functions, and Built-in Operations
Module 5: Functions & Modular Programming
- How to Define and Invoke Functions
- The return Statement vs. Standard Printing
- Managing Parameters and Arguments (Keyword & Arbitrary Arguments)
- Creating User-Defined and Nested Functions
- Practical, Real-World Function Applications
Module 6: Object-Oriented Programming (OOP) in Python
- Classes and Objects: Defining Classes, Instantiating Objects, and Real-World Modeling
- Exploring the __init__, self, and super() Keywords
- Inheritance Models: Single, Multiple, Multilevel, and Hierarchical Inheritance
- Polymorphism: Implementing Method Overloading and Overriding
- Encapsulation: Managing Public, Private, and Protected Access Modifiers
- Data Abstraction: Working with Abstract Base Classes (ABC) and Abstract Methods
Module 7: Numerical Computing with NumPy
- Introduction and Installation of NumPy
- Working with the N-dimensional Array (ndarray)
- Array Creation, Data Types, and Attributes
- Indexing, Slicing, and Advanced Indexing Techniques
- Array Broadcasting and Iteration
- Array Manipulation and Splitting
- Core NumPy Functions: Binary, String, Mathematical, Statistical, and Arithmetic Operations
- Sorting, Searching, Counting, and Byte Swapping
- Matrix Libraries and Linear Algebra Fundamentals
Module 8: Data Analysis with Pandas
- Pandas Setup and Architecture overview
- Understanding Pandas Series, DataFrames, and Panels
- Core Functionalities and Descriptive Statistics
- Data Reindexing, Iteration, and Sorting
- Handling and Cleaning Text Data
- Data Selection, Filtering, and Indexing
- Advanced Pandas Features: Window Functions, Date/Time Operations, Timedeltas, and Categorical Data
- File Input/Output (I/O) Tools and Basic Data Visualization via Pandas
Module 9: Data Visualization
- Introduction to Matplotlib
- Crafting Visual Narratives from Data
Module 10: Mathematical Foundations & Statistics
- Basic Statistics:
- Descriptive vs. Inferential Statistics
- Variable Types, Measurement Scales, and Sampling Techniques
- Frequency Distributions, Bar/Pie Charts, Box Plots, and Histograms
- Central Tendency (Mean, Median, Mode) and Dispersion (Variance, Standard Deviation)
- Outlier Detection, Skewness, Normal Curves, and Z-scores
- Probability Theory:
- Probability Basics, Addition, and Multiplication Rules
- Permutations and Combinations
- Discrete/Continuous Random Variables and Probability Distributions
- Advanced Statistics:
- Binomial and Normal Distributions
- Correlation Analysis (Pearson & Spearman)
- Central Limit Theorem and Confidence Intervals
- Hypothesis Testing (Null/Alternative Hypotheses, Type I & II Errors, One/Two-Tailed Tests)
Module 11: Machine Learning Foundations
- CPU vs. GPU architecture for Data Processing
- Supervised vs. Unsupervised Learning (Classification, Regression, Clustering)
- Model Evaluation Metrics and Understanding Errors
- Exploring ML Frameworks (Scikit-Learn, TensorFlow, Keras)
- Exploratory Data Analysis (EDA)
- The Bias-Variance Tradeoff
Module 12: Supervised Machine Learning Algorithms
- Linear Regression:
- Mathematical Core and Scratch Implementation
- Building Models with Scikit-Learn
- Calculating Errors: Mean Squared Error (MSE), Absolute Error
- Logistic Regression:
- Math and Scratch Implementation
- Scikit-Learn Integration
- Classification Metrics: Accuracy, Precision, Recall, F1-Score
- Decision Trees:
- Architecture (Roots, Internal Nodes, Leaves) and Splitting Criteria (Gini, Entropy, Info Gain)
- Algorithms (ID3, CART, C4.5/C5.0)
- Overfitting Prevention (Pre/Post Pruning)
- Handling Missing Values and Categorical Data
- Evaluating via Confusion Matrix and ROC/AUC curves
- Random Forests:
- Ensemble Learning (Bagging, Boosting, Stacking)
- Architecture, Bootstrapping, and Feature Randomness
- Out-of-bag (OOB) Estimation and Feature Importance
- Hyperparameter Tuning and Bias Mitigation
Module 13: Natural Language Processing (NLP)
- Core Terminology: Corpus, Tokens, and N-grams
- Tokenization Techniques (Whitespace, Regex)
- Text Normalization (Stemming vs. Lemmatization)
- Part-of-Speech (POS) Tagging
Module 14: Deep Dive into Transformers
- Embeddings: One-hot Encoding vs. Word Embeddings (Word2Vec, GloVe), Positional Embeddings, and Subword Tokenization (BPE).
- Encoder Architecture: Self-Attention Mechanisms, Layer Normalization, and Feed-Forward Networks.
- Decoder Architecture: Masked Self-Attention, Contextual Understanding, and Autoregressive Generation.
- The “Attention Is All You Need” Paper: Analyzing the 2017 breakthrough, overcoming RNN/LSTM limits, and understanding the foundation of modern models like BERT and GPT.
Module 15: Prompt Engineering
- Core Structure and Components of Effective Prompts
- Strategies: Zero-shot, Few-shot, and Chain-of-Thought Prompting
- Utilizing Prompt Templates and Variables
- Testing, Iteration, Best Practices, and Avoiding Common Pitfalls
Module 16: Building with LangChain
- Framework Overview, Installation, and Architecture (Models, Prompts, Chains, Agents)
- Text Processing: Summarization, Sentiment Analysis, Translation, and Content Generation
- Memory Management: Buffer, Summary, Window, and Entity Memory
- LLM Chains: Sequential, Router, and MapReduce Chains
- RAG & Question-Answering: Vector Stores, Embeddings, Document Splitting, and Context Citing
- Chatbot Development: Conversation Flow, Persona Customization, and Fallback Handling
- AI Agents: ReAct Agents, Tool Integration, and State Management
- Evaluating Responses, A/B Testing, and Cost Optimization
Module 17: Exploring Large Language Models (LLMs)
- Detailed breakdown of API structures, costs, limits, pros/cons, and Fine-Tuning for:
- OpenAI Models
- Meta Llama
- Anthropic Claude
- DeepSeek
Module 18: Local LLMs & Deployment (OLLAMA)
- Downloading and Running Local Models
- Environment Setup, Tokenizers, and GPU Configuration
- Terminal Usage, CLI Prompts, and Tuning Parameters (Temperature, Top-p)
- Python/Transformer API Integration
- Performance Optimization and Model Quantization
- Deployment Best Practices: Error Logging, API Security, Scaling, and Production Considerations
Your Instructor: Jai
Surya
Jai Surya has 10 plus years of hands-on experience building AI and machine learning systems inside production environments at Amazon, Infosys, LogiGen, Justdial, and AI Walkers. He has shipped enterprise automation pipelines, LLM integrations, and AI-driven decision systems that operate at scale under real business pressure.
His teaching philosophy is built around one non-negotiable standard: every concept must connect to something you can build and deploy. He does not lecture from documentation. He teaches from engineering decisions he has personally made in environments where performance failures cost real money.
VKNOWTECH AI has trained over 30,000 students across 15 countries with a 99% program completion rate. That completion rate exists because direct mentorship works. You get live access to Jai Surya throughout the 90 days, not a teaching assistant reading from a prepared script.
Generative AI Course Pricing and
Delivery Formats
- ONLINE LIVE TRAINING
$120 USD
(INR 9,999)
- Live instructor-led sessions
- Real-time Q&A every module
- 4 portfolio projects
- Generative AI Specialist Certification
- Morning and evening batches available
- RECORDED ACCESS
$60 USD
(INR 5,000)
- Lifetime replay access
- All 18 modules included
- Self-paced, no deadlines
- Re-watch any session
- Generative AI Specialist Certification
All three formats include the complete 18-module curriculum, all four portfolio
projects, and the VKNOWTECH AI Generative AI Specialist Certification. The format
determines how you learn, not what you learn or what you earn from it.
Does This Qualify for Employer Reimbursement?
Yes. Enrollees in the US and UK regularly have tuition covered through Learning and Development budgets. Request a formal invoice and course outline from our team. We prepare all supporting documentation required for HR or L&D submission within 24 hours.
Corporate Generative AI Training for NYC
Organizations
Per Glassdoor Salary Data 2026, Generative AI Engineers in NYC earn $95,000 to $140,000 at entry level, with senior AI Solutions Architects commanding $160,000 to $220,000. Every week your team goes untrained is a week a competitor’s team produces AI-powered output faster.
VKNOWTECH AI provides custom corporate AI training for teams of any size. Delivery is available live online, onsite in New York City, or through a blended model. Curriculum focus is customized by role: developers get the full engineering stack, marketing teams get prompt engineering and content automation workflows, data teams get LLM evaluation and RAG architecture.
Corporate clients receive a dedicated cohort schedule, a named account manager, post-training implementation support, and a completion report documenting the skills each team member demonstrated through live project delivery.
What Global Alumni Say
VKNOWTECH AI has trained professionals across India, the US, the UK, and 15 other countries
through live online delivery. The following is from enterprise trainees and working
professionals within the global alumni network.
- GLOBAL ENTERPRISE TRAINEE
- GLOBAL ALUMNI
- GLOBAL ENTERPRISE TRAINEE
Frequently Asked Questions: Generative AI
Training in New York City
What is the best generative AI training program available in New York City?
VKNOWTECH AI combines foundational depth with live project delivery and an instructor with 10 plus years at Amazon and Infosys. The program covers Python through LLM deployment in 90 days at $120 USD online, which is the strongest curriculum-to-cost ratio in the NYC generative AI training market right now.
How long does it take to go from zero experience to deploying a generative AI application?
With VKNOWTECH AI’s 90-day program, you move from Python fundamentals to four production-grade AI applications. The sequenced curriculum ensures every module builds on the previous one. Professionals who complete the program have deployable LLM projects ready for GitHub and technical job interviews by Day 90.
How is VKNOWTECH AI different from Noble Desktop or General Assembly NYC?
Noble Desktop’s Generative AI Certificate costs $3,495 for 78 hours. General Assembly’s Data Science Bootcamp runs $16,450 for 12 weeks. VKNOWTECH AI delivers 90 days of live instructor-led training at $120 USD. The instructor is a production AI practitioner. You build four real LLM applications, not one capstone project.
Can my company enroll an entire team for corporate generative AI upskilling in New York City?
Yes. VKNOWTECH AI provides corporate group training at custom pricing with role-specific curriculum delivery. Teams in finance, media, and SaaS have used this program to ship internal AI tools. Onsite delivery in New York City and live online delivery for distributed teams are both available with dedicated account management.
What tools are covered in the 90-day generative AI curriculum?
The full stack covers Python, NumPy, Pandas, Scikit-Learn, TensorFlow, OpenAI API, Google Gemini, Meta LLaMA, Anthropic Claude, DeepSeek, LangChain, vector databases, RAG architecture, Zapier AI, AutoGPT, and OLLAMA for local LLM deployment. Every tool is used inside a real production project, not an isolated exercise.
Is there a refund policy if the program does not meet expectations?
VKNOWTECH AI offers a 100% money-back guarantee after the first two demo sessions. Attend, evaluate the instruction quality and curriculum depth in person, and request a full refund if it does not meet the standard you came for. The financial risk sits entirely on VKNOWTECH AI, not on you.
What AI engineering salaries can NYC professionals expect after completing this training?
Per Glassdoor Salary Data 2026, Generative AI Engineers in NYC earn $95,000 to $140,000 at entry level. Senior AI Solutions Architects command $160,000 to $220,000. This program builds the LLM engineering and RAG architecture portfolio these roles require. Outcomes depend on experience, interview performance, and the specific role pursued.
The companies dominating their
markets in 2027 are building AI
capability right now.
New York City is the center of finance, media, healthcare, and tech in the United States. Every
one of those industries is being rebuilt around generative AI. The professionals who get
trained first will lead the projects, set the standards, and command the salaries that reflect
that expertise.