Hyderabad, India
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Build practical skills in Generative AI and Agentic AI with a structured learning path covering modern AI concepts, LLMs, RAG, AI applications and intelligent agent workflows. Explore practical learning designed for students, developers and IT professionals looking to build AI capabilities.
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Generative AI is changing how software, content, data and business applications are built. A practical Generative AI course can help learners understand how modern AI systems work and how they can be applied to real-world use cases.
Eduzek's Generative AI training in Hyderabad is designed to introduce learners to key areas such as large language models, prompt engineering, embeddings, retrieval-augmented generation (RAG), AI application development and Agentic AI concepts.
The learning path is relevant for software developers, IT professionals, students and professionals who want to build practical Generative AI skills. Along with Generative AI fundamentals, the course introduces Agentic AI and AI agents, helping learners understand how AI systems can move beyond generating responses to performing tasks through structured workflows and tools.
If you are looking for Generative AI training in Hyderabad, this course provides a structured way to explore the technologies, concepts and practical applications shaping modern AI development.
Developers looking to build applications powered by Generative AI and large language models.
Developers who want to explore Generative AI applications and AI-powered workflows.
Java professionals interested in applying modern AI capabilities to software applications.
Python developers looking to work with LLMs, RAG, AI applications and agentic workflows.
Data professionals looking to expand their skills into Generative AI and AI application development.
Technology professionals looking to understand and apply Generative AI in practical scenarios.
Students who want to build foundational and practical skills in modern AI technologies.
Professionals exploring a structured path into Generative AI and Agentic AI.
Explore the core concepts and practical areas covered in Generative AI, from LLM fundamentals and prompt engineering to RAG, AI applications and Agentic AI.
Build a conversational AI chatbot using a large language model to understand user queries and generate relevant responses.
Build a Retrieval-Augmented Generation application that allows users to upload documents and ask questions based on their content.
Create an AI-powered application that generates structured content such as articles, summaries, product descriptions and other text based on user prompts.
Develop an AI application that analyzes resumes, extracts relevant information and provides insights based on a selected job profile.
Build an AI agent capable of understanding a task, planning the required steps and using available tools to complete the task.
Create an Agentic AI workflow where multiple tasks are coordinated to complete a real-world objective through structured AI-driven actions.
| Course Name | Generative AI & Agentic AI Training |
|---|---|
| Training Mode | Online (Live) / Classroom / 1:1 / Corporate |
| Course Level | Beginner to Advanced |
| Duration | 100 - 130 Hours (Indicative) |
| Certification | Course Completion Certificate |
| Prerequisites | Basic programming knowledge is helpful; Python fundamentals are covered in the course. |
| Who Can Join | Developers, Data Professionals, AI/ML Enthusiasts and Beginners interested in Generative AI and Agentic AI |
| Key Technologies | Python, LLMs, Prompt Engineering, RAG, Pinecone, FAISS, Milvus, LangChain, LangGraph, LangSmith, Hugging Face, AutoGen, CrewAI, MCP |
| Generative AI Topics | Generative AI, LLMs, NLP, Prompt Engineering, Fine-Tuning, LoRA, QLoRA, Quantization and Multimodal AI |
| Agentic AI Topics | AI Agents, Agentic RAG, Multi-Agent Systems, LangGraph, AutoGen, CrewAI and MCP |
| Deployment & MLOps | FastAPI, Streamlit, Gradio, Docker, APIs, Observability, LangSmith and LangFuse |
| Hands-on Projects | Practical AI applications, Agentic RAG, multi-agent workflows and an end-to-end AI agent capstone project |
| Training Formats | Group Batch, 1:1 Training and Corporate Batch |
| Public Batch | Live instructor-led online training |
| Corporate Training | Customized training based on team tools, workflows, skill level and requirements |
| Trainer | Industry-expert and certified trainers |
Learn from industry practitioners and certified trainers who bring practical insights, real-world scenarios and structured guidance to every session.
Build practical skills through hands-on learning covering LLMs, Prompt Engineering, RAG, vector databases, AI agents and real-world AI applications.
Follow a structured learning path from Python, ML and NLP foundations to LLMs, RAG, LangChain, LangGraph, Agentic AI, fine-tuning and AI agent development.
Apply your learning through practical development work, including Agentic RAG, multi-agent workflows and an end-to-end AI agent capstone project.
Get practical exposure to technologies such as LangChain, LangGraph, LangSmith, Pinecone, FAISS, Milvus, Hugging Face, AutoGen, CrewAI and MCP.
No prior AI experience is required. The program covers Python and machine learning fundamentals before progressing into advanced Generative AI and Agentic AI concepts.
Choose from live group batches, 1:1 personalized training or corporate programs designed around specific team requirements and workflows.
Go beyond concepts and learn how to build, deploy and monitor AI applications using FastAPI, Streamlit, Gradio, Docker and observability tools.
Career Opportunities After Generative AI Training
Generative AI is expanding the way software applications are built, how businesses automate workflows, and how organizations work with data and knowledge. As companies adopt Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI copilots and autonomous AI agents, professionals with practical Generative AI skills can explore a growing range of roles across software development, artificial intelligence, machine learning, data and automation.
Completing a Generative AI Training in Hyderabad can help learners build the technical foundation required to develop and deploy AI-powered applications rather than only learning how to use generative AI tools. Eduzek's Generative AI Course in Hyderabad covers the progression from Python, machine learning and NLP fundamentals to LLMs, prompt engineering, RAG, vector databases, AI agents, fine-tuning, multimodal AI and production deployment. The program is designed for developers, data professionals and AI/ML enthusiasts and progresses from beginner to advanced concepts.
Generative AI Career Roles
Depending on your existing technical background, project experience and specialization, the skills developed through Generative AI training can be applied to several technology roles.
Generative AI Engineer: Develop applications powered by LLMs and generative AI models. Typical work can include prompt engineering, model integration, RAG pipelines, evaluation, AI application development and deployment.
AI Engineer / AI Developer: Build intelligent software applications that integrate machine learning models, LLM APIs, retrieval systems and AI automation into business workflows.
LLM Engineer: Work with Large Language Models to build, evaluate, customize and integrate LLM-powered applications. Skills can include prompt engineering, model evaluation, fine-tuning, embeddings, quantization and responsible AI practices.
RAG Engineer: Design Retrieval-Augmented Generation systems that connect LLMs with private or domain-specific information. This can involve document processing, embeddings, vector databases, retrieval strategies, context management and response evaluation.
Agentic AI Engineer: Build AI agents capable of planning, using tools, maintaining context and completing multi-step tasks. An Agentic AI Course in Hyderabad can be particularly relevant for learners interested in LangGraph, LangChain, AutoGen, CrewAI, MCP and multi-agent workflows.
AI Application Developer: Create practical applications such as intelligent assistants, enterprise chatbots, knowledge-base systems, document intelligence applications, AI-powered search and workflow automation tools.
NLP / Conversational AI Engineer: Apply natural language processing, embeddings, transformers and LLM technologies to build conversational interfaces, intelligent assistants and language-based applications.
Machine Learning Engineer with GenAI Skills: Professionals who already understand machine learning can extend their skill set into LLMs, generative models, RAG, fine-tuning and agentic systems, creating a broader AI engineering profile.
AI Automation Engineer: Use LLMs, AI agents, APIs and workflow automation to reduce repetitive processes and connect intelligent systems with existing business applications.
AI Solutions Developer: Translate business requirements into AI-powered solutions by selecting appropriate models, retrieval architectures, agent frameworks, APIs and deployment approaches.
Why Agentic AI Is Creating New Career Paths
The evolution from standalone AI models to systems that can reason through tasks, use tools and coordinate multiple steps is creating another important area of specialization: Agentic AI.
In a traditional generative AI application, a user may submit a prompt and receive a generated response. Agentic AI systems can go further by combining LLMs with tools, memory, retrieval, workflows and external services to complete more complex tasks.
This is why Agentic AI Training in Hyderabad can complement conventional Generative AI learning. Learners can move beyond prompt-based applications and understand how to design AI agents, agentic RAG systems and multi-agent workflows.
Eduzek's curriculum includes LangChain and LangGraph, agent development, Agentic RAG, knowledge graphs, multi-agent workflows, AutoGen, CrewAI and MCP. It also includes deployment technologies such as FastAPI, Streamlit, Gradio and Docker, helping learners understand the journey from an AI prototype to a deployable application.
Career Opportunities Across Different Experience Levels
The career path after Generative AI training can vary according to your previous experience.
For freshers and students, the focus can be on building strong foundations in Python, machine learning, NLP, LLMs and AI application development. Practical projects can help demonstrate skills through a portfolio rather than relying only on a course certificate.
For software developers, Generative AI can become an additional specialization on top of existing programming experience. Developers can move toward LLM application development, RAG engineering, AI integration, AI automation and agent development.
For data professionals and data scientists, Generative AI provides opportunities to work with embeddings, vector databases, LLM evaluation, retrieval systems, fine-tuning and AI-powered data applications.
For machine learning professionals, the transition can involve adding LLM engineering, generative models, multimodal AI, RAG and agentic architectures to an existing ML skill set.
For working IT professionals, an AI-focused specialization can be used to expand an existing software, cloud, data or development profile into emerging AI application and automation use cases.
Skills That Can Strengthen Your Generative AI Career
Employers hiring for AI-focused roles typically look beyond basic familiarity with ChatGPT or other AI tools. Practical engineering skills become increasingly important when building production-oriented applications.
A strong learning path can therefore include:
Python programming and AI fundamentals Machine learning and deep learning concepts Natural Language Processing and transformers Large Language Models and prompt engineering Embeddings and vector databases Retrieval-Augmented Generation (RAG) LangChain and LangGraph AI agents and Agentic AI Multi-agent architectures Fine-tuning with techniques such as LoRA and QLoRA Open-source models and Hugging Face Multimodal AI Model evaluation and responsible AI FastAPI, Streamlit and Gradio Docker and application deployment AI observability and monitoring AutoGen, CrewAI and MCP
These are also the major areas covered in Eduzek's current Generative AI and Agentic AI curriculum.
Build a Portfolio, Not Just a Certificate
A Generative AI certification can demonstrate course completion, but practical projects can provide stronger evidence of what you can actually build. For this reason, learners should focus on developing projects that demonstrate complete AI application workflows.
Examples include a RAG-based document assistant, enterprise knowledge chatbot, AI research assistant, intelligent customer-support application, multi-agent workflow, AI automation system or an end-to-end autonomous AI agent.
Eduzek's program includes hands-on agent development, Agentic RAG, knowledge graphs, model evaluation, deployment and a capstone project focused on building and deploying an end-to-end AI agent.
Generative AI Training in Hyderabad for Long-Term AI Skills
Hyderabad is an established technology and services hub, making practical AI and software engineering skills relevant across a wide range of technology-focused organizations. However, a career in Generative AI is not limited to a particular city. The same skills can be applied to AI product companies, software organizations, technology services, startups and remote teams.
For learners comparing AI courses in Hyderabad, it is useful to look beyond the number of tools listed in a syllabus. A more valuable learning path should connect fundamentals, LLM application development, RAG, Agentic AI, deployment and project work into one progression.
Eduzek's Generative AI Training Institute in Hyderabad follows this broader approach by combining Python and AI foundations with Generative AI, LLMs, RAG, Agentic AI frameworks, fine-tuning, multimodal AI and deployment. The course is offered at beginner-to-advanced level with an indicative duration of 100–130 hours, and training is available through group, 1:1 and corporate formats.
Where Generative AI and AI/ML Skills Meet
Learners researching AI and ML courses in Hyderabad may also consider how Generative AI fits into the broader artificial intelligence landscape. Machine learning, deep learning and NLP provide important foundations, while Generative AI adds capabilities for language, code, images and other generated content. Agentic AI extends these capabilities by combining models with tools, memory, retrieval and task-oriented workflows.
This makes a modern AI career path increasingly multidisciplinary. Instead of learning a single AI tool, professionals can develop a combination of programming, machine learning, LLM engineering, retrieval, agent development and deployment skills.
Start Building Your Generative AI Career
The objective of Generative AI training should not simply be to understand what an LLM is. It should be to develop the ability to identify an AI use case, select an appropriate model or architecture, connect it with relevant data, build an application, evaluate its output and deploy it responsibly.
With a structured Generative AI Course in Hyderabad, learners can progressively develop these capabilities and explore career paths such as Generative AI Engineer, AI Engineer, LLM Engineer, RAG Engineer, AI Application Developer, Agentic AI Engineer, NLP Engineer and AI Automation Engineer.
For professionals who want to go further, combining Generative AI with Agentic AI Training in Hyderabad can provide exposure to autonomous agents, multi-agent systems, Agentic RAG and modern agent frameworks. The result is a broader technical foundation for building the next generation of AI-powered applications.
“I would like to thank Ganesh for his excellent training in PowerApps and Power Automate. Shout out to his patience and knowledge. Ganesh is extremely dedicated, knowledgeable, and professional. He delivered more than what he promised in the demo session and cleared all my doubts. Worth the money!”
“Eduzek is a great platform to learn Power BI, PowerApps, and Power Automate for working professionals. The course tutorial and duration are designed in a perfect way. The teaching staff is very supportive and flexible in terms of class timing.”
“Eduzek helped me in learning Power Automate. I just completed my training and you can reach Ganesh anytime in case of queries and get them resolved, which I liked the best.”
“Excellent training from Ganesh on PowerApps, Power Automate, and SharePoint. I highly recommend Eduzek institute to get trained on these topics.”
“Good training and clearing the doubts with live project examples...”
Generative AI Training teaches you how to understand, build and deploy applications powered by Large Language Models (LLMs) and other generative AI technologies. The training typically covers Python, machine learning, NLP, LLMs, prompt engineering, RAG, vector databases, fine-tuning, multimodal AI, AI agents and deployment.
Generative AI Training in Hyderabad can help developers, data professionals, students and AI enthusiasts build practical skills in one of the fastest-growing areas of artificial intelligence. A structured program can provide hands-on exposure to LLMs, RAG, AI agents, LangChain, LangGraph, vector databases and production-oriented AI application development.
The Generative AI course covers Python and machine learning foundations, NLP, generative AI fundamentals, Large Language Models, prompt engineering, RAG, vector databases, LangChain, LangGraph, LLM evaluation, fine-tuning, multimodal AI, AI agents, multi-agent frameworks, deployment and a capstone project.
The course is designed for developers, data professionals, AI/ML enthusiasts and learners who want practical skills in Generative AI and Agentic AI. Basic programming familiarity is helpful, but prior AI experience is not required.
Yes. The program is designed for beginner-to-advanced learners and covers Python and machine learning fundamentals as part of the curriculum. You do not need prior Generative AI experience to start.
Basic programming exposure is helpful. Python fundamentals are also covered during the course, so prior expertise in Generative AI or advanced machine learning is not required.
Eduzek's Generative AI and Agentic AI Training has an indicative duration of 100–130 hours. The exact schedule and hands-on project structure can vary by batch.
You will learn Python and AI foundations, NLP, transformers, Generative AI, LLMs, prompt engineering, RAG, embeddings, vector databases, LangChain, LangGraph, LLM evaluation, fine-tuning with LoRA and QLoRA, multimodal AI, AI agents, multi-agent frameworks and deployment.
Yes. LLMs are a major part of the curriculum. The course covers LLM fundamentals, prompt engineering, embeddings, RAG, evaluation, open-source models, fine-tuning and practical LLM application development.
Agentic AI refers to AI systems designed to perform tasks using capabilities such as reasoning, tools, memory, workflows and external services. The course introduces AI agents, Agentic RAG, multi-agent workflows and frameworks such as LangGraph, AutoGen and CrewAI.
Generative AI focuses on models that generate content such as text, code, images or other outputs. Agentic AI builds on these capabilities by using AI models with tools, memory, workflows and other components to perform multi-step tasks. Learning both provides a broader understanding of modern AI application development.
Yes. Eduzek's Generative AI and Agentic AI Training includes a dedicated Agentic AI curriculum covering AI agents, LangChain, LangGraph, Agentic RAG, knowledge graphs, multi-agent workflows, AutoGen, CrewAI and MCP.
The Agentic AI component covers AI agent concepts, agent development, LangChain, LangGraph, Agentic RAG, knowledge graphs, multi-agent workflows and frameworks such as AutoGen, CrewAI and MCP. Learners also work toward building and deploying an end-to-end AI agent.
Yes. Retrieval-Augmented Generation (RAG) is an important part of the curriculum. You learn RAG concepts and pipelines, embeddings, vector databases, retrieval-based applications, Agentic RAG and knowledge graphs.
The course covers popular vector database technologies including Pinecone, FAISS and Milvus. These technologies are introduced in the context of embeddings, retrieval and RAG-based AI applications.
Yes. The curriculum includes LangChain architecture, agents and memory, along with LangGraph for building workflows and multi-agent systems. LangSmith is also covered for application development, evaluation and observability.
Yes. The course covers LLM fine-tuning concepts and techniques including LoRA, QLoRA and quantization. It also includes model evaluation and exposure to open-source LLMs through Hugging Face.
Yes. Advanced topics include multimodal LLMs and diffusion models for image and video generation, along with reinforcement learning fundamentals for AI systems.
Yes. Building AI agents is a core component of the program. Learners work with agent frameworks and concepts including LangChain, LangGraph, AutoGen, CrewAI, Agentic RAG and multi-agent workflows, with an end-to-end AI agent included as the capstone.
Yes. The program is designed as hands-on training and includes practical agent development, RAG, knowledge graphs, model evaluation, deployment and a capstone project. Exact hands-on projects can vary by batch.
The curriculum includes a capstone project in which learners build and deploy an end-to-end AI agent. The project brings together concepts from Generative AI, Agentic AI, frameworks and deployment.
Yes. The deployment module covers model serving with FastAPI, Streamlit and Gradio, along with Docker, deployment strategies and observability tools such as LangSmith and LangFuse.
The curriculum includes Python, machine learning, NLP, LLMs, Pinecone, FAISS, Milvus, LangChain, LangGraph, LangSmith, Hugging Face, AutoGen, CrewAI, MCP, FastAPI, Streamlit, Gradio and Docker.
Generative AI skills can be applied to roles such as Generative AI Engineer, AI Engineer, AI Application Developer, LLM Engineer, RAG Engineer, Agentic AI Engineer, NLP/Conversational AI Engineer and AI Automation Engineer. The opportunities you qualify for depend on your existing experience, technical skills and project portfolio.
Yes. Software developers are one of the target learner groups for the program. Developers can use the training to add LLM application development, RAG, AI agents, automation and AI deployment capabilities to their existing programming skills.
Yes. Data professionals can use the course to expand their existing skills into LLMs, embeddings, vector databases, RAG, model evaluation, fine-tuning and AI-powered applications.
Yes. Eduzek provides a course completion certificate after completing the Generative AI and Agentic AI Training. Certification is available across the listed training formats.
Eduzek offers Group Batch, 1:1 Training and Corporate Batch formats. Public group batches are conducted online, while 1:1 and corporate training can be delivered offline based on request.
Yes. Eduzek offers 1:1 Generative AI Training with private sessions that can be paced and scoped according to the learner's goals and timeline. The 1:1 format is available through a custom quote.
Yes. Eduzek offers corporate training that can be tailored to a team's tools, workflows, skill level and requirements. Corporate training is available through a custom quote and can include certification.
Eduzek's program combines Generative AI and Agentic AI in one structured learning path, covering fundamentals, LLMs, RAG, vector databases, LangChain, LangGraph, fine-tuning, multimodal AI, AI agents and deployment. The program is beginner-to-advanced, hands-on and taught by industry-expert and certified trainers, with group, 1:1 and corporate training options.
Talk to our team about batch timings, course details, and how this training fits your background.