Advanced Generative AI And Agentic AI Training In Hyderabad helps professionals master LLMs, RAG, and multi-agent systems for real-world AI solutions.
Level
Advanced
Duration
8 weeks



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Advanced Generative AI And Agentic AI Training In Hyderabad by TechPratham equips professionals with enterprise-level skills in LLM integration, Retrieval-Augmented Generation (RAG), and multi-agent system design. Hyderabad, especially tech hubs like HITEC City, Gachibowli, and Madhapur, hosts global companies such as Microsoft, Deloitte, Accenture, and TCS that actively invest in AI-driven automation. This training focuses on real-world architecture, cloud deployment, AI security, performance optimization, and scalable system design. Learners gain hands-on project experience aligned with Hyderabad’s fast-growing IT ecosystem, preparing them for high-demand generative and agentic AI roles in multinational organizations.
Working professional who is carrying more then 10 years of industry experience.
Access to updated presentation decks shared during live training sessions.
E-book provided by TechPratham. All rights reserved.
Module-wise assignments and MCQs provided for practice.
Daily Session would be recorded and shared to the candidate.
Live projects will be provided for hands-on practice.
Expert-guided resume building with industry-focused content support.
Comprehensive interview preparation with real-time scenario practice.
Python for AI & Automation (Foundation Layer)
Python for AI & Automation (Foundation Layer) builds core Python skills to automate tasks and create a strong base for AI and machine learning development.
Introduction to Agentic AI & LLM Ecosystem
A foundational overview of how autonomous AI agents work with large language models to plan, reason, and execute tasks across modern AI systems.
Basics of LangChain and LangGraph
An introductory overview of using LangChain and LangGraph to build, connect, and orchestrate intelligent, multi-step AI workflows.
Python SDK and Node Configuration
An overview of setting up the Python SDK and configuring nodes to build, connect, and manage scalable AI workflows efficiently.
Exploring LangGraph Core Concepts
A concise introduction to building stateful, multi-step AI workflows using LangGraph for agent orchestration and decision-making.
Multi-Agent Setup with LangGraph
A practical introduction to designing and orchestrating multiple AI agents that collaborate and share state using LangGraph.
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AI-Powered Enterprise Knowledge Assistant – HITEC City (Microsoft Use Case)
In this project, learners design a Generative AI-based enterprise knowledge assistant inspired by corporate environments like Microsoft in HITEC City, Hyderabad. The system retrieves internal documents, summarizes policies, and provides contextual business insights using advanced LLM and RAG architecture. Key Highlights: • Build Retrieval-Augmented Generation (RAG) pipelines with vector databases. • Implement secure document indexing and semantic search systems. • Design prompt engineering strategies to reduce hallucinations. • Deploy scalable APIs for integration with enterprise platforms.
Multi-Agent Workflow Automation – Gachibowli (Amazon Use Case)
This project focuses on building an Agentic AI system inspired by tech enterprises like Amazon in Gachibowli, Hyderabad. Learners create collaborative AI agents capable of task planning, delegation, and autonomous execution across business workflows. Key Highlights: • Design multi-agent architecture with memory and reasoning capabilities. • Integrate REST APIs, databases, and cloud-based services. • Implement workflow monitoring dashboards for task tracking. • Optimize system scalability, reliability, and response latency.
AI Analytics & Decision Intelligence Platform – Financial District (Deloitte Use Case)
In this project, learners develop an AI-powered analytics platform inspired by consulting firms like Deloitte in the Financial District, Hyderabad. The system analyzes datasets, generates executive insights, and automates report creation using advanced Generative AI techniques. Key Highlights: • Build AI-driven data summarization and insight generation pipelines. • Integrate Python-based analytics and visualization tools. • Implement structured report generation with validation layers. • Apply governance controls to ensure compliance and output accuracy.

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