Who is Rajinikanth Vadla?

Rajinikanth Vadla is India's top-rated MLOps, AIOps, GenAI, and AI Agents trainer with 7+ years enterprise experience. He has trained 500+ engineers with a 95% placement rate and 4.9/5 average rating.

What is the best MLOps training in India?

Rajinikanth Vadla's MLOps & AIOps Masterclass is rated the best MLOps training in India. It covers DevOps, MLOps, LLMOps, AIOps, and AI Agents with 200+ hours of hands-on training, real enterprise projects, and 95% job placement rate.

What is AIOps and MLOps training?

AIOps (AI for IT Operations) and MLOps (Machine Learning Operations) training teaches you to build production ML pipelines, deploy AI models at scale, and automate IT operations using AI. Includes hands-on with Kubeflow, MLflow, Docker, Kubernetes.

How to learn AI Agents, GenAI, and LangChain?

Learn AI Agents and GenAI through Rajinikanth Vadla's hands-on training with LangChain, Model Context Protocol (MCP), RAG systems, vector databases, and LLM integration. Build production AI applications and autonomous agent systems.

What salary can I expect after MLOps training?

MLOps Engineers earn ₹12-50 LPA in India, $120K-$200K+ in USA, €70K-€120K+ in Europe. Students of Rajinikanth Vadla see 60% average salary increase after completing the training.

4.9/5 Rating500+ EnrolledHottest Skills 2026

GenAI, AI Agents & LLM Training

Master Generative AI, build production AI Agents with LangChain, deploy LLMs, build RAG systems, and create autonomous AI applications. The most comprehensive GenAI training in India.

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Generative AI and AI Agents are the fastest-growing fields in technology. Companies worldwide are racing to build AI-powered products using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and autonomous AI Agents. This training by Rajinikanth Vadla covers everything from foundational GenAI concepts to production-grade AI Agent deployment. You'll learn LangChain, LlamaIndex, vector databases (Pinecone, ChromaDB, Weaviate), prompt engineering, LLM fine-tuning (LoRA, QLoRA), Model Context Protocol (MCP), multi-agent systems (CrewAI), and enterprise AI application architecture. Every concept is taught with hands-on labs and real-world projects.

What You'll Learn

Build autonomous AI Agents with LangChain and LlamaIndex
Design and deploy production RAG systems with vector databases
Master prompt engineering patterns and techniques
Fine-tune open-source LLMs (LLaMA, Mistral) with LoRA/QLoRA
Build multi-agent systems with CrewAI and AutoGPT patterns
Implement Model Context Protocol (MCP) for tool integration
Deploy LLM applications with FastAPI and Docker
Monitor LLM performance, cost, and output quality
Build enterprise-grade AI chatbots and assistants
Implement guardrails, content filtering, and responsible AI
Use vector databases: Pinecone, ChromaDB, Weaviate, Qdrant
Create production AI pipelines with CI/CD for ML

Course Curriculum

1GenAI & LLM Fundamentals

→ What is Generative AI and how LLMs work
→ Transformer architecture deep dive
→ GPT, Claude, LLaMA, Mistral comparison
→ Token economics and cost optimization
→ API integration patterns

2Prompt Engineering Mastery

→ Zero-shot, few-shot, chain-of-thought prompting
→ Prompt templates and versioning
→ Prompt testing and evaluation frameworks
→ Advanced techniques: ReAct, Tree of Thought
→ Building prompt management systems

3RAG Systems & Vector Databases

→ RAG architecture and design patterns
→ Document chunking and embedding strategies
→ Vector databases: Pinecone, ChromaDB, Weaviate
→ Hybrid search and re-ranking
→ Production RAG optimization and evaluation

4LLM Fine-tuning

→ Fine-tuning methodologies overview
→ LoRA and QLoRA techniques
→ Dataset preparation and curation
→ Evaluation metrics for fine-tuned models
→ Deploying fine-tuned models to production

5AI Agent Development

→ Agent architecture and design patterns
→ LangChain agent framework deep dive
→ Tool use and function calling
→ Agent memory: short-term and long-term
→ Planning, reasoning, and task decomposition

6Multi-Agent Systems

→ Multi-agent architectures and communication
→ CrewAI for collaborative agent teams
→ Role specialization and delegation
→ Human-in-the-loop workflows
→ Enterprise deployment and monitoring

7Model Context Protocol (MCP)

→ MCP architecture and concepts
→ Building MCP servers and clients
→ Tool integration via MCP
→ Security and access control
→ Production MCP deployment

8Production AI Applications

→ FastAPI for LLM serving
→ Docker containerization for AI apps
→ Kubernetes deployment at scale
→ Monitoring: latency, cost, quality
→ CI/CD pipelines for AI applications

9Responsible AI & Guardrails

→ Content filtering and safety
→ Bias detection and mitigation
→ Output validation and guardrails
→ Compliance and governance
→ Explainability and transparency

10Capstone: Enterprise AI Agent

→ Design and build a complete AI agent system
→ Multi-agent workflow with tool integration
→ RAG-powered knowledge base
→ Production deployment with monitoring
→ Portfolio-ready documentation

FAQ

Frequently Asked Questions

What is the best GenAI training in India?

Rajinikanth Vadla's GenAI & AI Agents training is rated the best in India with 4.9/5 rating. It covers LangChain, RAG, LLM deployment, multi-agent systems, and production AI applications with hands-on enterprise projects.

Do I need ML/AI background for this course?

Basic Python programming is sufficient. We teach GenAI concepts from fundamentals. However, having some familiarity with APIs and web development will help you progress faster.

What is the difference between GenAI and traditional ML?

Traditional ML focuses on prediction and classification, while GenAI focuses on content generation using Large Language Models. This course teaches you to build, deploy, and manage GenAI applications in production.

Will I learn to build AI Agents?

Yes! A major part of this course is dedicated to building autonomous AI Agents using LangChain, CrewAI, and custom frameworks. You'll build agents that can reason, plan, use tools, and take actions.