+91 95534 06450
Info@akirait.com
Whatsapp Instagram Icon-facebook Icon-linkedin Youtube
Contact Us

GenerativeAI ProfessionalTraining Program

60 Hours

Live Online

LLMs, RAG, Fine-Tuning & AI Agents

Become a job-ready Generative AI professional with hands-on training in LLMs, prompt engineering, RAG pipelines, fine-tuning, multimodal AI and autonomous AI agents, backed by expert mentorship and a portfolio-ready capstone project.
Download Brochure

About the Generative AI Training Full Course

Akira Global Technologies’ Generative AI Professional Training Program is designed to equip software developers, data analysts, ML engineers, product managers, tech leads and career switchers with the skills needed to work confidently across the full Gen AI stack, in just 60 hours. This Generative AI course covers the complete journey from foundational machine learning and deep learning concepts to transformer architecture, large language models, prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning with PEFT and LoRA, multimodal generative AI and autonomous AI agents, along with industry-standard tools like OpenAI, Anthropic Claude, Hugging Face, LangChain, LlamaIndex and leading vector databases.

Whether you are a software developer looking to specialize in Gen AI, a data analyst or ML engineer expanding into large language models, a product manager who wants to understand the technology powering AI products, or a career switcher moving into artificial intelligence, this Generative AI training program is 70% hands-on and 30% theory, with every module built around labs, notebooks and mini-projects, so you graduate with practical, deployable Gen AI skills rather than theory alone.

This is not just a Generative AI training course on paper, it is a lab-focused, mentor-supported program that culminates in a real-world capstone project where you design, build and deploy a complete Gen AI application, so you finish ready to apply your skills to real business use cases across healthcare, finance, retail and customer support.

Course Details Information
Duration 60 Hours
Format 20 sessions x 3 hours, or 30 sessions x 2 hours (flexible)
Mode Classroom / Virtual Live / Hybrid
Curriculum Full Gen AI Stack – Foundations to LLMs, RAG, Fine-Tuning & Agents
Project Real-World Capstone Project (RAG App, AI Agent, or Multimodal App)
Methodology 70% Hands-On Labs, Notebooks & Mini-Projects, 30% Theory
Certification Certificate of Completion + Capstone Project Evaluation
Tools Covered OpenAI, Anthropic (Claude), Hugging Face, LangChain, LlamaIndex, Vector Databases
Who Should Enroll?

Software Developers

Developers who want to specialize in building Generative AI applications

Data Analysts & ML Engineers

Professionals looking to expand into LLMs, RAG and fine-tuning

Product Managers & Tech Leads

Anyone who needs a working understanding of the Gen AI stack to guide product and technical decisions

What You'll Get

When you join Akira Global Technologies’ Generative AI Training Program, you get complete support at every step, from learning to landing a job.

Training

  • 60 Hours of Instructor-Led Training with Hands-On Labs
  • Full Gen AI Stack Curriculum — Foundations, LLMs, RAG, Fine-Tuning & Agents
  • Tool-Agnostic and Tool-Specific Training (OpenAI, Anthropic Claude, Hugging Face)
  • Latest Agentic AI Trends including Tool-Calling and Model Context Protocol (MCP)

Hands-on Experience

  • Notebook and Lab-Based Practice in Every Module
  • Two Mini-Projects (Post-RAG Module and Post-Agents Module)
  • Real-World Capstone Project — Design, Build and Deploy a Complete Gen AI App
  • Portfolio-Ready Projects for GitHub and LinkedIn Showcase

Get Certified

  • Certificate of Completion
  • Capstone Project Evaluation
  • Module-End Quizzes for Continuous Assessment

Responsible AI Focus

  • Dedicated Module on Ethics, Bias, Safety and Governance
  • Regulatory Landscape Overview including the EU AI Act
  • Enterprise-Ready Responsible AI Checklist

Mentor Support

  • Small Batch, Mentor-Supported Learning
  • Doubt-Clearing Sessions
  • Industry Case Studies from Healthcare, Finance, Retail and Customer Support

Career Assistance

  • Post-Course Resource Kit and Cheat Sheets
  • Community Access
  • Final Presentation and Instructor Feedback

Enroll Now - Get All These Benefits

Generative AI Training Course Syllabus

Our Generative AI training course syllabus runs for 60 hours, moving from AI and Gen AI foundations through Python and machine learning refreshers, deep learning and NLP fundamentals, transformer architecture, prompt engineering, RAG pipelines, fine-tuning with PEFT and LoRA, AI agents and agentic workflows, multimodal generative AI, responsible AI and governance, and deployment and MLOps, before wrapping up with a real-world capstone project.

Prerequisites:

  • Basic programming knowledge, Python preferred, is required to begin
  • No prior AI or ML experience is mandatory — concepts are covered from the ground up
Module Topics Covered Hours Tools/Project
Module 1: Introduction to AI & the Generative AI Landscape
  • Evolution of AI from rule-based to Generative AI, what is Generative AI, overview of foundation models (GPT, Claude, Gemini, LLaMA, Mistral), real-world applications and career pathways
 3 Hours Hands-on:
Exploring outputs of different Gen AI models
Module 2: Python & Machine Learning Foundations Refresher
  • Python essentials for AI (NumPy, Pandas), core ML concepts, introduction to neural networks, working with Jupyter/Colab and GPU basics
 4 Hours Hands-on: Build and train a simple neural network classifier
Module 3: Deep Learning & Neural Network Foundations
  • Feedforward networks, CNNs and RNNs/LSTMs (brief), embeddings and vector representations, introduction to attention mechanisms, loss functions and optimizers
 5 Hours Hands-on:
Visualize word embeddings and attention weights
Module 4: NLP Fundamentals
  • Text preprocessing and tokenization (BPE, WordPiece, SentencePiece), language modeling basics, Word2Vec, GloVe and contextual embeddings, NLP evaluation metrics
 4 Hours Hands-on: Build a tokenizer and compare tokenization strategies
Module 5: Transformer Architecture & LLM Internals
  • Self-attention and multi-head attention, encoder-only, decoder-only and encoder-decoder architectures
  • LLM training (pre-training, SFT, RLHF), scaling laws and context windows
 7 Hours Hands-on:
Implement a simplified self-attention mechanism from scratch
Module 6: Prompt Engineering & In-Context Learning
  • Zero-shot, few-shot and chain-of-thought prompting, ReAct, self-consistency and tree-of-thought techniques, system prompts and output formatting, structured outputs and function/tool-calling prompts
 6 Hours Hands-on: Build a prompt library for a real business use case
Module 7: Retrieval-Augmented Generation (RAG)
  • Why RAG, embeddings and vector similarity search, vector databases (Pinecone, Chroma, FAISS, Weaviate), building RAG pipelines with LangChain/LlamaIndex, chunking, re-ranking and RAG evaluation
 8 Hours Tools: LangChain, LlamaIndex, Vector Databases.
Hands-on: Build a full document Q&A chatbot using RAG
Module 8: Fine-Tuning, PEFT & Model Customization
  • When to fine-tune vs prompt engineer vs use RAG, full fine-tuning vs PEFT, LoRA and QLoRA, instruction tuning and dataset preparation, introduction to RLHF and DPO
 6 Hours Tools: Hugging Face PEFT library.
Hands-on: Fine-tune a small open-source LLM using LoRA
Module 9: AI Agents & Agentic Workflows
  • What makes an agent (reasoning, planning, memory, tool use), agent frameworks (LangGraph, CrewAI, AutoGen), tool-calling patterns, Model Context Protocol (MCP), multi-agent orchestration
 6 Hours Hands-on: Build an autonomous agent that uses tools to complete multi-step tasks
Module 10: Multimodal Generative AI
  • Text-to-image generation and diffusion models (conceptual), image generation APIs and visual prompting, text-to-speech, speech-to-text and text-to-video overview, multimodal LLMs
 4 Hours Hands-on: Build a mini multimodal application
Module 11: Responsible AI, Safety, Ethics & Governance
  • Bias, fairness and hallucination in generative models, data privacy, copyright and IP considerations, AI safety principles and guardrail design, regulatory landscape (EU AI Act)
 3 Hours Discussion: Case studies of Gen AI failures and lessons learned
Module 12: Deployment, Evaluation, MLOps & Capstone Project
  • Deploying LLM applications via APIs, containers and serverless options, LLMOps basics (monitoring, logging, cost, latency), evaluation frameworks, capstone project design, build and presentation
 4 Hours Tools: FastAPI, Docker.
Project: End-to-end Gen AI capstone application

Real-World Capstone Project (Included)

Participants design, build and present a complete end-to-end Generative AI application, choosing from options such as:

A RAG-based document or knowledge assistant
An autonomous AI agent that performs multi-step tasks using tools
A multimodal Gen AI application combining text, image, audio or video

Generative AI Training Admission Process

1

Fill Inquiry Form

Share your details and course interest

2

Counselling Call

Speak with our admissions team about your goals and fit

3

Get Course Access

Receive login credentials and join the orientation session

Requirements

Eligibility Criteria:

  • Basic programming knowledge, Python preferred, is required
  • No prior AI or ML experience is mandatory — foundational concepts are taught from the ground up
  • Open to software developers, data analysts, ML engineers, product managers, tech leads and career switchers into AI
Success

Thank you! Form submitted successfully.

This field is required
This field is required
This field is required
  • DevOps Engineer Course
  • Data Engineering Course
  • Business Analyst Course
  • AI & Machine Learning Course
  • Salesforce Training Course
  • Generative AI Training Program
  • Cloud Computing Training Program
  • Cybersecurity Training Course
  • ServiceNow Training Course
  • Data Analytics Training Program
  • Tableau, Power BI & SQL Training Course
  • Other Akira IT Courses
This field is required

Learn From Generative AI Industry Experts

GST

Generative AI Trainer

years of experience building and deploying Generative AI applications across real-world industry projects. Expert in LLMs, prompt engineering, RAG pipelines, fine-tuning and AI agents. Focuses on hands-on labs and a real-world capstone project to make participants job-ready.

LLMs

prompt engineering

RAG pipelines

fine-tuning

AI agents

Assignments & Assessments

Module-End Quizzes (Formative Assessment)
Two Mini-Projects (Post-RAG Module and Post-Agents Module)
Final Capstone Project with Presentation

Training Outcome

By the end of this Generative AI training course, participants will be able to:
Understand the architecture and working of Large Language Models (LLMs)
Design effective prompts using advanced prompt engineering techniques
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases
Fine-tune and customize open-source LLMs using PEFT and LoRA techniques
Design and deploy AI agents capable of tool use and multi-step reasoning
Work with multimodal generative AI across text, image, audio and video
Apply responsible AI practices, including bias mitigation and safety guardrails
Deploy and monitor Gen AI applications in production environments

Tools & Technologies You'll Master

Gemini
HuggingFace
LangChain
LlamaIndex
Chroma Streamline Icon: https://streamlinehq.com
Pinecone Icon Streamline Icon: https://streamlinehq.com
LangGraph
CrewAI
Colab
Testimonials

What Our AI & Machine Learning Learners Say

"The supervised learning and model tuning modules gave me a real feel for how ML gets built in practice, not just theory from a textbook. Presenting my capstone project in the final module was the moment I felt genuinely job-ready."

Rohan Kulkarni

Machine Learning Engineer at a fintech company

"I came in comfortable with Excel and SQL but nervous about Python. The Python and statistics modules built my confidence step by step, and by the deep learning module I was building CNNs on my own."

Ishita Bansal

Data Analyst transitioning into AI/M

"Strong course overall, especially the hands-on labs after every single topic. I'd have liked a little more time on NLP, but the capstone project more than made up for it when it came to interviews."

Aditya Menon

CS Graduate, now Junior ML Engineer

"The deployment module was what really differentiated this course for me. Learning to actually ship a model as an API instead of just training it in a notebook made my portfolio stand out."

Sneha Kulkarni

Working Professional switching into Data Science

"No prior ML background going in, just some basic Python. The way the modules built from statistics to classical ML to deep learning made everything click, and the mentorship during lab hours was genuinely useful."

Varun Chandra

Fresher, now AI Engineer

"Good structured program covering classical ML, deep learning, NLP, and computer vision all in one track. The mid-program mini-project was a great checkpoint to see how much I'd actually absorbed."

Priyanka Rathi

Analyst upskilling into Machine Learning

FAQ'S

Frequently Asked Questions

The Generative AI training program duration is 60 hours, delivered as instructor-led sessions in a flexible format of either 20 sessions of 3 hours each or 30 sessions of 2 hours each.