What makes this AI Engineering & Infrastructure course different from a traditional AI or Machine Learning course?

This course focuses on how AI systems are designed, implemented, as well as monitored and expanded in production. Beyond AI basics, you'll be taught LLM applications including RAG AI agents, cloud infrastructure deployment, AIOps, and AI design and development through guided projects.

Which AI technologies and concepts will I learn?

The curriculum includes LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG), AI Agents, AI deployment, cloud infrastructure containers, GPUs, AIOps monitoring model drift, as well as responsible AI.

Is this course suitable for engineering students from non-CS backgrounds?

Yes. The curriculum is designed to suit students in Electronics, Mechanical, Electrical, Civil, as well as other engineering disciplines. The concepts are presented from first principles.

What kind of projects will I add to my portfolio?

The course will include guided projects as well as an end-of-course capstone in which you develop an end-to–end AI system that includes the deployment of data infrastructure, monitoring, and operational issues. These projects will help you build your professional resume and portfolio.

What is Retrieval-Augmented Generation (RAG)?

RAG is a process that enables AI assistants to find relevant information from documents before producing responses that are more precise, contextual, and trustworthy.

How does this course help me understand Large Language Models (LLMs)?

You'll discover the ways LLMs operate, the reasons why hallucinations happen, as well as how grounding increases the reliability of LLMs, and how advanced technologies such as the RAG system, prompt engineering, and AI agents can be used to create real-world AI applications.

AI Engineering & Infrastructure

Learn about LLMs, RAG, AI agents, as well as model deployment and cloud infrastructure by following guided projects.
Learn the process by which AI models are constructed, then deployed, monitored, and implemented in real-world production environments.
From modeling and data through deployment and monitoring, as well as AI infrastructure.
Learn about containers, GPUs, cloud infrastructure, and orchestration. Also, learn about auto-scaling through hands-on demonstrations.
Learn about model serving strategies for rollout, edge inference, and monitoring, and the reliability of AI systems.
Industry experts will be presenting, along with exclusive IIT/NIT masterclasses by faculty, and an end-of-course closing.
Develop expertise in cloud deployment, cloud infrastructure Monitoring, AIOps, and AI design of systems.

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Online Live

Mode of Delivery

2 Months

Course Duration

₹ 20,000+GST

Course Fee

Nov 1, 2026

Start Date

What you'll learn

Master Prompt Engineering, Retrieval-Augmented Generation (RAG), and AI Agents to Build Production-Ready AI Applications.
Understand How GPUs, Cloud Platforms, Containers, Orchestration, and Auto-Scaling Power Modern AI Infrastructure.
Gain Practical Knowledge of AI Monitoring, Model Drift, CI/CD, AIOps, and Troubleshooting Production AI Workloads.
Build AI Deployment and AI Agent Projects Through Guided, Instructor-Led Hands-On Templates.
Understand AI Security, Bias, Fairness, Privacy, Governance, and Cost Optimization for Responsible AI Systems.
Design an End-to-End AI Solution Demonstrating Deployment, Monitoring, Governance, and Operational Best Practices.

Skills you'll gain

AI Engineering Fundamentals Large Language Model (LLM) ApplicationsRetrieval-Augmented Generation (RAG)Prompt EngineeringAI Agent Fundamentals AI Model Evaluation AI Model Packaging & VersioningAI Model DeploymentAI Serving & InferenceAI Infrastructure FundamentalsCloud Infrastructure for AIGPU & Compute FundamentalsContainers & OrchestrationInfrastructure as CodeAI Monitoring & ObservabilityModel Drift DetectionAIOps FundamentalsCI/CD Concepts for AIResponsible AI (Bias, Fairness & Privacy)

Earn a Career Certificate

Receive a Professional Certificate from EICTA Consortium upon successful completion of the program. Showcase this credential on your LinkedIn profile, resume, CV, and professional portfolio to demonstrate your expertise in AI engineering, AI infrastructure, LLM applications, AI deployment, and production-ready AI systems.

Course Fee

Rs. 20000.00 +GST

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Learn From Industry Experts and IIT/NIT Faculty

Learn from experienced AI practitioners and distinguished IIT/NIT faculty members who provide real-world knowledge into AI infrastructure, engineering deployment, and production AI systems.

Build AI Systems That Go Beyond the Model

Understand how the latest AI applications are developed and deployed, monitored, and scaled by LLMs, RAG, AI agents, cloud infrastructure, along with production workflows.

Guided Projects and Industry-Style Capstone

You'll complete the three AI projects, and then an instructor-led capstone in which you'll develop an operational AI system, which covers deployment infrastructure, monitoring, and AI risks.

Learn Production AI, Not Just AI Theory

Explore beyond the concept of machine learning to learn how enterprise AI systems are designed, implemented, and monitored, optimized, as well as maintained within real-world settings.

Gain Skills for the Next Generation of AI Careers

Learn practical skills in AI Engineering, AI infrastructure, LLM applications, cloud deployment, AIOps, and AI design skills. These are skills that are increasingly sought-after by employers.