⚡AI Engineering Cohort

Learn AI Engineeringin 10 Weeks

A hands-on program for software engineering upskilling into FDE and AI engineering roles.

18th July → 20th September

Sat-Sun (9:00 PM - 10:30 PM IST)

10 Weeks

Trusted by 300 engineers in our first cohort from

In 10 weeks: Learn AI → Ship to Production

I deployed 2 major projects to production. I am thankful for the learning curve that I have gone through from the internals and fundamentals of AI, to depth of Vector DB, RAG, Agentic architectures!

Rajul Babel

Principal Engineer at Habuild | Ex-Flipkart, Paytm, Amazon

This cohort has helped me to elevate my career to the next level. Thanks for building basic knowledge of AI in my career. I really hope to take this to the next level.

Neel Desai

Principal Member of Technical Staff at Oracle, US

Grateful to the instructors Gaurav Sen, Tanishq and the cohort for pushing the bar every week. The shift for me? Moving from using LLMs to engineering systems around them — multi-agents, RAG, evals, and guardrails.

Fahad Khan

Staff Software Engineer at Visa

The curriculum covered both the foundations and practical aspects of building AI applications, and the capstone project was a great opportunity to put those learnings into practice. A big thank you to the AIEngg founder Gaurav Sen and the TAs for the guidance, feedback, and support throughout the journey. Looking forward to applying these learnings to real-world engineering and product challenges!

Lavanya Mothilal

Engineering Manager at SAP

A massive thank you to our instructor, Gaurav Sen, for designing such an intensive, no-nonsense curriculum and providing invaluable engineering insights throughout the cohort. This cohort wasn’t just about high-level abstractions, it was a rigorous, code-first journey that pushed me to understand modern AI architecture from the ground up.

Dwaipayan Gupta

Vice President (Data & AI) at Wells Fargo

I extend my gratitude to Gaurav Sen, Tanishq, Aryan, and Samit for the thoughtful discussions and knowledge sharing that enhanced my learning throughout this journey. What stood out during this program was the shift from understanding concepts to building with them — designing RAG pipelines, orchestrating multi-agent systems, and integrating tools into functional AI products.

Carmal Shaktan

Senior Cloud and AI Engineer at IBM

Cohort Outline

A structured approach to AI implementation

Week 1

Week 1:

Week 1: Overview of LLMs & Training

Understand the fundamental building blocks of LLMs with tokenization, vectorization and attention.

Tokenisation, Vectorization, Attention
Pre-training and post-training
LLM Evaluations
End-to-end LLM lifecycle

tokensvectorsattentionLLMs

Week 2

Week 2:

Week 2: Quantization and Fine-Tuning

Learn how LLMs are quantized for fast processing, and how to fine-tune models to meet specific business requirements.

Post-training
Quantization - FP16, attention optimizations
Fine Tuning - LoRA/QLoRA
Dataset prep → training → evaluation

LLM optimizationsquantizationLORAfine-tuning

Week 3

Week 3:

Week 3: Retrieval Augmented Generation

Learn chunking strategies, data ingestion, reranking, indexing, vector databases, and other techniques for retrieval augmented generation.

RAG: chunking strategies, data ingestion, reranker, indexer
Vector Embeddings, Vector Databases
Search Algorithms: ANN algorithms (HNSW, IVF)

RAGVector DBVector Search

Week 4

Week 4:

Week 4: Hands-on RAG Implementation

An interactive project where students learn to code a RAG-based application and learn best practices for AI safety.

Reranking strategies, Query rewriting, HyDE
Input and output guardrails
Safety: Prompt injection, Intent classification
Coding Assignment: Build a RAG chatbot using API calls

RAGsafetyguardrailscoding

Week 5

Week 5:

Week 5: AI Agents and Tool Calling

Learn what an Agent is, how they are different from plain LLMs, Tool Calling, ReAct pattern, and Agent Orchestration.

LLM vs Agent vs Multiple Agents
ReAct pattern
Prompt Chaining, Orchestration, Routing
Coding Assignment: Customer support agent

agentsreActorchestrationcoding

Week 6

Week 6:

Week 6: MCP, Context Engineering, Multi-Agent Systems

Code an AI Agent with MCP and memory, optimizing agentic flow.

Context Engineering
Memory in Agents
Model Context Protocol
Multi-Agents
Coding Assignment: MCP with memory and optimising agentic flow

agentic memorymcpcontext engineeringmemory systems

Week 7

Week 7:

Week 7: Evals, AI Applications in Production

Learn how Evals are used in production AI applications, and best practices for AI development.

Evals: How to avoid hallucinations with Evals
LLM as a Judge
Tradeoffs and design decisions
Fine-tuning vs Prompting vs RAG
Project: Build your own LLM Judge

EvalsLLM as JudgehallucinationsAI in practice

Week 8

Week 8:

Week 8: Agentic System Design

Learn how AI agents are scaled in distributed systems, and the system design of large-scale AI applications.

Agents at scale
MCP vs. API wrappers
Design tradeoffs
Best practices for agentic system design

AgentsMCPSystem Design

Week 9

Week 9:

Week 9: Image and Reasoning Models

Learn how multimodal models are trained with images and video, and the mechanism of diffusion-based models.

Multimodal models
CLIP
Video Models
CoT, RLHF

clipmultimodalimagesrlhf

Week 10

Week 10:

Week 10: Capstone Project

Create a production-grade AI project on a topic of your choice.

Recap important concepts
Problem selection
Metrics for evaluation
Feedback on completed projects

capstoneprojectevaluation

Instructor

Your Cohort Instructor

Gaurav Sen

Software Engineer | Founder, AIEngg

Gaurav Sen is a Software Engineer with experience designing and building AI systems at InterviewReady. He has also worked with companies like Docker and NeonDB in explaining how to build reliable AI systems. Gaurav has previously spoken at the University of Houston-Texas, IIT Gandhinagar, and BITS Hyderabad.

What You Will Learn

Key Takeaways

AI skills that are essential and job-relevant.

Building Reliable AI systems

Learn how to de-risk and productionize AI applications using guardrails and model evaluations.

Industry-relevant AI skills

Learn the technology behind real-world systems like Agents, MCP, and vector databases.

Identify AI opportunities

Learn how to assess AI use cases specific to your product and team needs.

Communicate effectively across teams

Master the vocabulary, tradeoffs, and design patterns that come up in AI engineering discussions.

Build Real AI Projects

Understand and apply Agentic AI and RAG Systems through hands-on, real-world projects.

Evaluate and Judge AI Systems

Build production-grade AI with guardrails, LLM-as-a-Judge evaluations, and observability.

Investment

Cohort Investment

AI Engineering Cohort

$1,400

$1,750Early Bird Discount 20% OFF

Cohort Starts On Jul 17, 2026

20 Live Classes with Instructor

9 Weekly Networking Sessions

90 days of implementation support

Lifetime access to recordings

Certificate of completion

7-day money-back guarantee