AI Engineering
& Agentic AI
Build, deploy and scale production-ready AI systems. Learn to engineer LLM applications, RAG systems, AI agents and real-world AI workflows through practical, hands-on learning.
Professional Technical Program
27K+ Students Enrolled
Rs. 40,000
+Taxes
AI Is Moving From Chatbots To AI Systems.
The next generation of AI applications isn't limited to generating text.
Modern AI systems can:
This program teaches you how to engineer those systems.
You won't just learn how to use AI.
You'll learn how to build it.
The AI Engineering Journey.
Software & AI Engineering Foundations
Build a strong foundation in modern software development using Node.js, TypeScript, NestJS and REST APIs. Learn to design secure, scalable backend applications and integrate AI capabilities into real-world software systems.
LLM Engineering
Learn to build intelligent applications powered by large language models. Explore prompt engineering, context management, structured outputs, model APIs and tool calling to develop reliable, efficient and production-ready LLM applications.
RAG & Knowledge Systems
Build AI systems that retrieve and use information from external knowledge sources. Learn document processing, embeddings, vector databases, semantic search, hybrid retrieval and reranking to create accurate, context-aware knowledge assistants.
AI Agents
Design intelligent AI agents capable of planning tasks, using tools, managing memory and executing workflows. Learn agent architecture, state management, human-in-the-loop controls and failure handling to build AI systems that go beyond simple conversations.
Multi-Agent Systems
Build coordinated teams of AI agents that communicate, collaborate and delegate responsibilities. Explore agent orchestration, supervisor-worker architectures, shared memory and parallel workflows to develop systems capable of handling complex, multi-step business tasks.
MCP & AI Integrations
Connect AI applications with real-world tools, databases, APIs and external services using Model Context Protocol (MCP). Learn to build secure integrations that enable AI agents to interact with business software, access information and execute practical workflows.
AI Evaluation & Security
Learn to evaluate AI system quality, reliability and security before production deployment. Explore LLM and RAG evaluation, guardrails, prompt injection protection, observability, logging and monitoring to build trustworthy AI applications.
Production AI
Take AI applications from development to real-world deployment using Docker, cloud platforms and CI/CD pipelines. Learn authentication, API security, caching, monitoring, scaling and cost optimisation to operate reliable AI systems in production environments.
AI Architecture
Learn to design scalable, secure and reliable AI systems for enterprise applications. Explore LLM, RAG and agent architectures, data pipelines, databases, event-driven systems, multi-tenancy and infrastructure planning to build production-ready AI solutions.
Production Capstone
Apply everything you have learned to design, build and deploy a complete AI system that solves a real business problem. Develop an end-to-end solution incorporating AI models, knowledge retrieval, agents, external integrations, evaluation and production deployment.
What Will You Build?
LLM Application
Enterprise Knowledge Assistant
Autonomous AI Agent
Multi-Agent AI Team
Tool-Using AI Worker
AI Evaluation & Security Layer
Cloud-Deployed AI Application
Enterprise AI Architecture
Production AI System
From AI Foundations To Production Systems.
Build and deploy your first AI-enabled API.
From Software Developer to AI Builder
Learn the software engineering foundations required to build modern AI applications.
Technology Stack.
Backend
Node.js • TypeScript • NestJS • REST APIs
LLMs
OpenAI • Anthropic • Gemini • Open-source LLMs
AI Engineering
LLMs • Context Engineering • Prompt Engineering • Structured Outputs • Function Calling
RAG
Embeddings • Vector Databases • PostgreSQL/pgvector • Semantic Search • Hybrid Search • Reranking
Agents
LangChain.js • LangGraph.js • Multi-Agent Architecture
Integrations
MCP • APIs • Databases • SaaS • External Tools
Production
Docker • AWS • Azure • CI/CD • Monitoring • Observability
AI Quality & Security
Evals • LLM-as-Judge • RAG Evaluation • Agent Evaluation • Guardrails • AI Security
Who Is This Course For?
Software Developers
Learn to integrate LLMs, RAG and AI agents into software products.
Backend Developers
Build APIs, AI workflows, tool integrations and production AI infrastructure.
Full-Stack Developers
Expand existing development skills into AI application engineering.
Data Scientists & ML Professionals
Move from experimentation toward production AI applications.
Technical Founders
Learn how to prototype and architect AI-powered products.
Developers Moving Into AI
Build a practical AI engineering portfolio through hands-on projects.
From Software Developer to AI Engineer.
Build the skills to engineer LLM applications, intelligent agents and production-ready AI systems through practical, hands-on learning.
Structured technical learning program.
Live instructor-led technical sessions.
Build practical AI engineering applications.
Design and deploy an end-to-end AI system.
Build Real AI Systems.
Learn AI engineering by building LLM applications, RAG systems, autonomous agents and production-ready AI workflows throughout the program.
LLM Application
Build a production-style LLM application using model APIs, context engineering, structured outputs and tool calling.
Enterprise Knowledge Assistant
Build a knowledge assistant that retrieves enterprise information and generates grounded answers using RAG systems.
Autonomous AI Agent
Create an intelligent agent capable of planning tasks, managing state, using tools and executing workflows.
Multi-Agent AI Team
Build a coordinated team of AI agents that communicate, delegate responsibilities and collaborate on research or business tasks.
Tool-Using AI Worker
Engineer an AI worker that interacts with external systems through MCP, APIs, databases and SaaS integrations.
AI Evaluation & Security Layer
Build an evaluation and observability layer to assess AI quality, monitor performance and implement safeguards.
Cloud-Deployed AI Application
Deploy an AI application to the cloud using containers, CI/CD, monitoring and production infrastructure.
Enterprise AI Architecture
Design a scalable and reliable AI system architecture for a real-world enterprise business problem.
Production AI System
Design, build and deploy an end-to-end AI system that solves a real business problem using the engineering principles taught throughout the program.
Build production-ready AI applications, RAG systems, autonomous agents and real-world AI workflows through structured live learning and practical projects.
60+ Live Learning Hours
Instructor-led technical sessions across the 10-week program.
6+ Hands-on Projects
Build LLM applications, RAG systems, AI agents and production AI workflows.
Production AI Capstone
Design and deploy an end-to-end AI system for a real business problem.
Certificate of Completion
Educity certificate upon successful completion of program requirements, projects and final capstone.
Production Engineering Skills
Learn deployment, evaluation, AI security, architecture and system reliability.
Tools & Technologies Covered.
Explore the development tools, AI frameworks, orchestration platforms and technologies used throughout modern AI engineering.
The ultimate guide to Web3 and Artificial Intelligence
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Attain Recognition with the Certificate of Course Completion
Upon completing the course, you will receive a certificate—an impactful addition to your LinkedIn profile that can capture the interest of our hiring partners and prominent big data companies.