Generative AI Training in UK: Live, Instructor-Led, and Built to Get You Hired

VKNOWTECH AI delivers a 90-day, live instructor-led Generative AI training program accessible fully online from anywhere in the UK. Led by Jai Surya, an enterprise AI professional with over 10 years of real-world experience, this program covers Python, LangChain, RAG architecture, and full LLM deployment, with documented alumni placements at Accenture, Deloitte, Capgemini, and Cognizant.

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The UK Tech Market Demands
Generative AI: Hiring Data and Skill Requirements for 2026

The skills gap is not approaching. It is already inside the organizations you want to work at. Enterprise hiring managers at consulting firms, financial services companies, and global technology organizations are rewriting job descriptions to require LangChain, RAG pipelines, and prompt engineering as hard technical competencies.

A surface-level awareness of AI is not what gets you through a technical screening at Accenture or Capgemini. The panel wants to know whether you can deploy a retrieval-augmented generation system, fine-tune a large language model, and build production-ready AI applications using tools their own engineering teams are already running.

VKNOWTECH AI‘s 90-day Generative AI program was engineered to close exactly that gap. Not theoretically. Through 18 structured modules of live, hands-on instruction delivered by an enterprise practitioner who has built these systems in real business environments.

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AT A GLANCE

Course Details	Information
Course Name	Generative AI Master Training Program
Trainer Name	Jai Surya
Trainer Experience	10+ Years Industry Experience
Next Batch Date	20 May 2026
Batch Timings	Morning & Evening
Training Mode	Online & Offline (Instructor-Led)
Course Duration	90 Days (3 Months)
Demo Class	20 May 2026 – Free Demo Session
Contact Number	+91 90100 91700
Email Address	admin@vknowtech.ai

30,000+

Students Trained

15+

Countries

50+

UK 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, countless other programs in London 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 UK L&D Manager

Your executive team read about 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 Career Pivot

You are in finance, marketing, product management, or data analysis in the UK. AI engineering roles on LinkedIn grew massively over the last 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

Do Not Just Get a Certificate: Build a Live
LLM Deployment Portfolio to Show UK
Employers

Academic programs hand you a certificate with a university logo. VKNOWTECH AI hands you
a portfolio with working code, deployed applications, and real technical output that you can
show in a technical interview.

By the end of the 90-day program, you will have built retrieval-augmented generation
pipelines, custom LangChain chatbots with persona and fallback handling, AI agent
workflows, and locally deployed LLMs with Ollama in a production configuration. These are
not classroom exercises. They are assets.

Global consulting firms and technology organizations do not shortlist candidates on a
certificate alone. They shortlist candidates who can demonstrate execution. Your portfolio
from this program is that demonstration.

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.

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 UK fintech and media companies are paying top sterling to build right now.

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 Finance Director.

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 the UK in Q1 2026. It is the most in-demand LLM engineering skill in the current market.

Ready to see what you'll build?

Talk to our team to access live project examples.

90 Days vs. 6-Week Theory: Why Our 18-ModuleGenAI Curriculum Outperforms Academic Certificates

Cambridge Advance Online charges over $2,900 for a 6-week program that teaches professionals to ‘identify AI opportunities.’ Imperial College’s short course helps you ‘champion innovation.’

Neither program teaches you to build a working LLM application, implement a RAG system, or deploy a local model using Ollama.

Cambridge Advance Online charges over $2,900 for a 6-week program that teaches professionals to ‘identify AI opportunities.’ Imperial College’s short course helps you ‘champion innovation.’

Neither program teaches you to build a working LLM application, implement a RAG system, or deploy a local model using Ollama.

From Python to Production: What the 18 Modules Actually Cover

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

Learn Directly from Jai Surya: 10+ Years of Enterprise AI and Architecture Experience

Jai Surya is not an academic. He is an enterprise AI practitioner who has spent over a decade building and deploying AI systems across real business environments. The training sessions he runs at VKNOWTECH AI are built entirely around implementation scenarios drawn from actual deployment challenges.

He teaches how LangChain behaves in production, what fails in RAG pipelines at scale, how to optimize LLM performance under real computational constraints, and what hiring managers at Accenture and Capgemini actually test candidates on. You are learning from someone who has faced those problems and solved them.

Sessions are live and interactive. Every student has direct access to Jai Surya throughout the 90-day program. Real questions get real answers from someone who has done the work, not someone who read the documentation.

Hired by the Big 4: How Our Alumni Secure Roles at Deloitte, Capgemini,
and Accenture

VKNOWTECH AI has trained over 30,000 learners. That number means nothing without proof of where they went after training ended.

Our alumni are working at Accenture, Deloitte, Capgemini, Cognizant, ADP, Concentrix, and DXC Technology. These are not aspirational logos on a brochure. They are the actual global organizations where VKNOWTECH AI graduates are building active AI careers right now. Every one of these organizations has major UK operations across London, Manchester, Edinburgh, and Birmingham.

The average package secured by placed alumni is 8.5 LPA. Across 26 plus hiring organizations and over 30,000 trained professionals, this is the single most outcome-backed generative AI training program available at this level of curriculum depth and instructor quality.

Our 3-Step Placement Engine: From Resume Optimization to Technical Mock Interviews

Claiming placement support is the most overused phrase in the online training industry. Every
low-tier course adds it to a landing page. Here is exactly what VKNOWTECH AI’s
placement structure looks like in practice, step by step.

Step 1 is resume rebuilding.

Your resume is rewritten to reflect the portfolio you built during the 90-day program. Your LangChain projects, RAG implementations, LLM deployment work, and Python data engineering experience are translated into the exact language that hiring managers at Accenture and Capgemini use in their screening process.

Step 2 is mock interviews.

You go through structured technical and behavioral interview simulations built around the actual interview formats used by the 26 plus organizations in our hiring network. You are tested on the same questions that appear in real screenings before you face a real panel.

Step 3 is active career mentoring and hiring connections.

The VKNOWTECH AI placement team connects you to relevant open roles within the network. You are not handed a PDF with job boards and told good luck. The support is active, structured, and continues until placement.

100% Live and Online: Flexible UK Batch Timings for Working Professionals

Every session is instructor-led. There are no pre-recorded videos substituting for live instruction. You attend Jai Surya’s classes in real time, ask questions in real time, and complete assignments that are reviewed and discussed in real time.

Morning and evening batch timings are available, structured for professionals in the UK who cannot pause their careers while they train. The program is fully accessible online whether you are based in London, Manchester, Birmingham, Leeds, or anywhere else across the UK.

The next batch starts 20 May 2026. A free demo class is available on the same date. One session is enough to understand exactly what the program covers and whether Jai Surya’s instruction style matches the way you learn.

The Full Technology Stack You Will Master by Day 90

This is not a prompt literacy program. It is not a ChatGPT awareness course. Here is the complete technology stack you will be proficient in by the time the 90 days are complete.

Language Models & APIs

ChatGPT, OpenAI API, Google Gemini, Meta Llama, Anthropic Claude, and DeepSeek.

Application Frameworks

LangChain with full memory management, router chains, map-reduce chains, and AI agent integration.

Vector Databases & RAG

Embeddings, document splitting, vector stores, and contextual citation retrieval systems.

Local Deployment

Ollama, model quantization, GPU configuration, tokenizer setup, Python
and Transformer API integration, API security, and production
scaling.

Data & ML Foundations

Python, NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, and Keras.

Every tool on that list appears in active UK job descriptions at the organizations where VKNOWTECH AI alumni work. Knowing how to prompt ChatGPT will not save your position in a restructure. Deploying Ollama and fine-tuning local models will.

Is This the Right Generative AI Training
for Your Career Goals?

This program is built for three types of professionals in the UK who are ready to transition
from AI awareness to active technical implementation.

Software Developers & Engineers

Add LLM application development to your technical profile and qualify for senior AI engineering roles at global technology firms.

Data Analysts & Professionals

Move beyond reporting and visualization into machine learning and generative AI implementation.

Career Changers in Tech

Establish a structured, mentored, 90-day pathway into the UK AI job market, backed by a real portfolio and active placement support.

The Standard

Global consulting firms do not care where your training institute is headquartered. They care whether you can build RAG architectures, deploy LangChain applications, and configure production LLM environments. VKNOWTECH AI trains you to do exactly that.

The Timeline

If a 6-week certificate for your LinkedIn profile is sufficient, there are cheaper options. If you want to build systems that pass technical screenings at the organizations hiring AI engineers right now, the next batch starts 20 May 2026.

Ready to advance your career?

Speak to an advisor about your goals.

Generative AI Course Pricing and Delivery
Formats

£95 GBP

($120 USD)

£50 GBP

($60 USD)

Custom

Contact for group rates

All 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.

Frequently Asked Questions

What does the VKNOWTECH AI Generative AI program cover for UK professionals?

The 90-day program runs 18 modules from Python fundamentals through NLP, Transformer architecture, LangChain, RAG systems, LLM comparison and fine-tuning, and local deployment with Ollama. Tools covered include ChatGPT, OpenAI, Google Gemini, Meta Llama, Anthropic Claude, and DeepSeek. Every module is built around real enterprise AI implementation, not academic theory.

Who teaches the VKNOWTECH AI Generative AI training program?

Jai Surya leads the Generative AI training at VKNOWTECH AI with over 10 years of enterprise AI and architecture experience. All sessions are live and instructor-led, not pre-recorded. Jai Surya teaches real implementation workflows drawn from actual business deployments, including production challenges and LLM optimization at scale.

Do VKNOWTECH AI graduates get placed at UK companies?

VKNOWTECH AI alumni work at global organizations including Accenture, Deloitte, Capgemini, Cognizant, ADP, DXC Technology, and Concentrix, all with significant UK operations. Over 30,000 learners have been trained across 26 plus hiring organizations. The average placed package is 8.5 LPA, with 100% placement assistance provided to every enrolled student.

Is the Generative AI training fully online and accessible from the UK?

Yes. The entire 90-day program is delivered live online and is fully accessible from anywhere in the UK. Morning and evening batch timings are available for working professionals. Every session is instructor-led with direct access to Jai Surya, real-time assignments, and structured feedback throughout the program duration.

How does VKNOWTECH AI compare to courses from Imperial College or Cambridge?

Imperial College and Cambridge programs focus on AI literacy and strategic awareness, typically over 6 to 12 weeks. VKNOWTECH AI delivers a full technical build program across 90 days, covering hands-on LLM development, LangChain applications, and production RAG deployment. The program ends with a real portfolio and active placement support, not only a certificate.

What does the placement support at VKNOWTECH AI actually involve?

Placement support includes resume rebuilding mapped to your technical portfolio, technical and behavioral mock interviews modeled on actual hiring formats at Accenture and Capgemini, career mentoring, and active connections to 26 plus hiring organizations. The process is structured and ongoing, not a one-time resume review or a passive job board referral.

When does the next Generative AI batch start and how do I enroll?

The next batch starts 20 May 2026, with a free demo session on the same date. Enroll or book your demo class via WhatsApp at wa.link/0w4git. You can also contact the VKNOWTECH AI team directly at admin@vknowtech.ai or call +91 90100 91700 to speak with a course advisor before committing.

The companies dominating their UK
markets in 2027 are building AI
capability right now.

The UK is a critical hub for finance, healthcare, and tech. 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.

NEXT BATCH

20 May 2026

FREE DEMO