AI Engineer
Roadmap 2025 🚀
Build Production Agentic AI, RAG pipelines, Fine-Tuning LLMs,
Reinforcement Learning, with 11 industry-level apps.
Himanshu Ramchandani
Microsoft - MVP
Detailed Roadmap:
https://god-level-python.notion.site/AI-Engineer-HQ-b3c9840
7b4ab45819811db081ae9d102?pvs=4
Curriculum
Prerequsites
- 7-Step AI Prep Challenge
1.Foundations of AI Engineering
2.Mastering Large Language Models (LLMs)
3.Retrieval-Augmented Generation (RAG)
4. Fine-Tuning LLMs
5.Reinforcement Learning and Ethical AI
6.Agentic Workflows
7.Career Acceleration
8.Bonus
Prerequsites
7-Step AI Prep Challenge
This challenge is for you to get started. You may already know about these topics
and can complete the challenge in 1 day as well.
Access the Challenge links here:
https://god-level-python.notion.site/7-Step-AI-Bootcamp-Prep-Ch
allenge-1c3ffb33c49580ef92eae98681a2ec6b?pvs=4
[Module - 1]
Foundations of AI Engineering
1.1 - Python
1.1.1 - [Hands-On] Functions & Higher Order Functions
1.1.2 - [Hands-On] Modules, Packages, Library &
Framework
1.1.3 - [Hands-On] OOPs [Object Oriented Programming]
1.1.4 - [Hands-On] Data Structures & Algorithms
1.1.5 - [Hands-On] Data Manipulation [NumPy & Pandas]
1.2 - Mathematics in AI
1.2.1 - Linear Algebra
1.2.2 - Calculus
1.2.3 - Statistics & Probability
1.3 - Overview of the AI Ecosystem
1.3.1 - AI and Its Evolution
1.3.2 - AI vs ML vs DL vs GenAI vs LLM vs ChatGPT vs RL
1.3.3 - LLM Ecosystem - ChatGPT, Grok, HuggingFace
1.3.4 - AI Market Analysis & Career Opportunity
1.3.5 - AI Use Cases & Tools
1.4 - Machine Learning as of 2025
1.4.1 - All you need to know about Machine Learning
1.4.2 - [Hands-On] Building a Classification Model
1.4.3 - [Hands-On] Building Multiple Linear Regression
model
1.4.4 - When to use Which ML Algorithm?
1.5 - Deep Learning as of 2025
1.5.1 - [Hands-On] Building Your First Neural Network
1.5.2 - [Hands-On] Activation Functions from Scratch
1.5.3 - Drawbacks in RNN, CNN, LSTM architecture
1.6 - The Project Lab [Build-Deploy-Market]
[The Project Lab - 01] AI-powered Resume Analyzer using
Python, Flask & NLP
[Thus Showcase] - Show your project publicly
[Community/YouTube/GitHub]
1.7 - Interview & Resources
Technical Interview Practice Questions
[Task] - Research Papers
[Module - 2]
Mastering Large Language Models (LLMs)
2.1 - LLM Ecosystem and Access
2.1.1 - Introduction to Transformer Architecture
2.1.2 - LLM Model Architectures
2.1.3 - How to train LLMs?
2.1.4 - [Cloud providers] Azure Open AI, AWS Bedrock,
GCP Vertex AI
2.1.5 - [Open-source LLMs] DeepSeek, LLaMA, Mistral 7b
(via Hugging Face)
2.1.6 - [Hands-On] Setup LLM on your Local machine
using Ollama
2.1.7 - [Hands-On] Sentiment classification pipeline for
Amazon product reviews
2.2 - Enterprise Applications
2.2.1 - Business problems solved by LLMs
2.2.2 - Workflow for developing LLM-based applications
2.2.3 - [Hands-On] Azure Open AI’s Python API to
generate text
2.2.4 - [Cost-benefit analysis] Cloud vs. on-premise
2.2.5 - [Hands-On] HR query bot and outline of workflow
2.2.6 - Multimodal AI Systems
2.2.6 - Vision Models
2.3 - Prompt Engineering
2.3.0 - What is prompt engineering?
2.3.1 - Zero-shot & Few-shot
2.3.2 - Chain-of-Thought & Tree-of-Thought
2.3.3 - Designing prompts for evaluation [LLM as a judge]
2.3.4 - [Hands-On] Design zero-shot and few-shot
prompts using Azure AI
2.3.5 - [Hands-On] CoT prompt to solve a math problem
2.4 - System Design
2.4.1 - The 7 Step ML System Design Framework
2.4.2 - Pinterest - Visual Search ML System
2.4.3 - How to build a GenerativeAI Platform?
2.5 - The Project Lab [Build-Deploy-Market]
[The Project Lab - 02] Building LLM from Scratch
[Thus Showcase] - Show your project publicly
[Community/YouTube/GitHub]
2.6 - Interview & Resources
Technical Interview Practice Questions
Resources
[Module - 3]
Retrieval-Augmented Generation (RAG)
3.1 - RAG Fundamentals & Workflow
3.1.1 - What is RAG? & Workflow
3.1.2 - Why RAG matters? Overcoming LLM limitations
3.1.3 - RAG Architecture
3.1.4 - [Hands-On] RAG demo using a pre-built tool -
LangChain
3.2 - Embeddings and Vector Databases
3.2.1 - What are Vector representations of Text
3.2.2 - How embeddings work? Word2Vec, BERT
3.2.3 - [Hands-On] Generate embeddings for sentences
using Hugging Face
3.2.4 - Vector Database Ecosystem overview - ChromaDB,
Pinecone, Postgres Vector
3.2.5 - [Hands-On] Vector database with Tesla 10-K
statements
3.3 - Advanced RAG
3.3.0 - Reranking and Structured Retrieval
3.3.1 - [Hands-On] Implement a basic RAG pipeline
3.3.2 - Workflow optimization - Balancing retrieval quality
and generation coherence
3.3.3 - Evaluating RAG Outputs
3.3.4 - [Hands-On] Tesla RAG
3.3.5 - Hybrid Search
3.3.6 - RAG evaluation [RAGAS]
3.4 - The Project Lab [Build-Deploy-Market]
[The Project Lab - 03] Finance Annual Report RAG Q&A
[Thus Showcase] - Show your project publicly
[Community/YouTube/GitHub]
3.5 - Interview & Resources
Technical Interview Practice Questions
[Module - 4]
Fine-Tuning LLMs
4.1 - Fine-tuning Fundamentals
4.1.1 - Why fine-tune? When is it beneficial?
4.1.2 - How transformers enable fine-tuning [Transfer
learning principles]
4.1.3 - [Hands-On] Preparing Data for Fine-Tuning
4.1.4 - [Hands-On] Fine-tune Mistral 7b on a
domain-specific dataset
4.2 - Parameter-Efficient Fine-Tuning (PEFT)
4.2.1 - What is PEFT?
4.2.2 - Low-Rank Adaptation [LoRA]
4.2.3 - [Hands-On] Fine-tune Mistral 7b with LoRA
4.2.4 - QLoRA
4.3 - Evaluation and Deployment
4.3.0 - Perplexity [language modeling], BERTScore
[semantic similarity]
4.3.1 - [Hands-On] Evaluating Mistral
4.3.2 - [Hands-On] Fine-tuning job cost estimate using
Azure ML pricing calculator
4.3.3 - [Hands-On] Deploy the fine-tuned Mistral 7b locally
4.3.4 - AI Cost Optimization
4.3.5 - Quantization
4.3.5 - [Hands-On] Quantize Supply Chain Forecaster
4.4 - The Project Lab [Build-Deploy-Market]
[The Project Lab - 04] Legal QnA - Domain Expert LLM
[Thus Showcase] - Show your project publicly
[Community/YouTube/GitHub]
4.5 - Interview & Resources
Technical Interview Practice Questions
[Module - 5]
Reinforcement Learning and Ethical AI
5.1 - Reinforcement Learning with Human Feedback
(RLHF)
5.1.1 - What is RLHF?
5.1.2 - Reward model & policy optimization [PPO]
5.1.3 - Limitations - Cost, subjectivity, scalability
5.1.4 - [Hands-On] Pre-trained RLHF model vs LLM
5.2 - RLHF Workflow and Implementation
5.2.1 - RLHF process
5.2.2 - [Hands-On] Simulate a RLHF cycle
5.3 - Ethical and Enterprise Considerations
5.3.0 - Bias and fairness
5.3.1 - [Hands-On] Gender stereotypes in text generation
5.3.2 - Content filtering
5.3.3 - [Hands-On] Toxicity filter using an LLM to flag
harmful outputs
5.3.4 - [Hands-On] Create a Model Card
5.4 - The Project Lab [Build-Deploy-Market]
[The Project Lab - 05] Ethical Chatbot
[Thus Showcase] - Show your project publicly
[Community/YouTube/GitHub]
5.5 - Interview & Resources
Technical Interview Practice Questions
[Module - 6]
Agentic Workflows
6.1 - Agentic Patterns
6.1.1 - What are agentic workflows?
6.1.2 - [Hands-On] Building First AI Agent from Scratch
6.1.3 - What is reflection?
6.1.4 - [Hands-On] Build a reflection agent using
LangChain
6.2 - Tool Use - Managing Agentic Memory
6.2.1 - Memory in agents
6.2.2 - [Hands-On] Agent with MemGPT to manage a
conversation history
6.3 - Tool Use - Function Calling with Agents
6.3.0 - Function calling
6.3.1 - [Hands-On] AI agent that calls a Hugging Face API
6.4 - Planning with Agents [ReAct Framework]
6.4.1 - What is planning?
6.4.2 - [Hands-On] Implement a ReAct agent to plan a
travel itinerary
6.5 - Multi-Agent Collaboration
6.5.1 - What is multi-agent collaboration?
6.5.2 - Models - Open AI Swarm [triage], Crew AI
[flow-based], LangGraph [graph-based]
6.5.3 - [Hands-On] Two-agent system using LangChain
6.5.4 - [Hands-On] Multi-agent system with LangGraph
for a Q&A task
6.5.5 - [Hands-On] Autonomous Systems
6.5.6 - [Hands-On] Reasoning Fraud Agent
6.5.7 - Model Context Protocol [MCP]
6.5.8 - Agent-to-Agent [A2A] Protocol
6.6 - The Project Lab [Build-Deploy-Market]
[The Project Lab - 06] Travel Booking Agent
[Thus Showcase] - Show your project publicly
[Community/YouTube/GitHub]
6.7 - Interview & Resources
Technical Interview Practice Questions
[Module - 7]
Career Acceleration
7.1 - Project with Mentoring
[The Project Lab - 07]
[1 - AI Tutor for Education]
[2 - AI Tutor for Education] RAG + Agentic
[3 - AI Tutor for Education] Planning agent to suggest
study topics
[4 - AI Tutor for Education] Evaluate the tutor with
queries
[Thus Showcase] - Show your project publicly
[Community/YouTube/GitHub]
7.2 - Portfolio Building
7.2.1 - GitHub Profile & Repositories
7.2.2 - Personal Website Building & Deployment
7.3 - Resume and Interview Prep
7.3.1 - Resume Template
7.3.2 - Resume Checklist
7.3.3 - Interview Preparation
7.3.4 - AI/ML Interview Questions
7.3.5 - LLMs Interview Questions
7.3.6 - Machine Learning Interview Questions
7.4 - Networking
7.4.1 - Engaging in Following AI Communities
7.4.2 - Follow these AI Creators on LinkedIn
7.4.3 - Follow these AI Creators on YouTube
7.5 - Personal Branding [Not recommended for
Everyone]
7.5.1 - LinkedIn Profile Optimization
7.5.2 - Sharing your work Online
7.5.3 - Cold Out Reach to Potential Clients/Recruiters
[Miscellaneous]
Bonus
Note: These bonus bundles will not be available anywhere else,
but only inside the course.
AI Job Navigator Toolkit [$500 Value]
Freelance AI Profit Blueprint [$2000 Value]
VIP Masterclass Pass [$2000 Value]
Post-Course Success Playbook [$1000 Value]
The Project Lab Bonus Bundle [Build-Deploy-Market]
[The Project Lab - 08] Healthcare Symptom Diagnostic
Agent
[The Project Lab - 09] E-Commerce Product
Recommendation Engine
[The Project Lab - 10] Supply Chain Optimization
Forecaster
[The Project Lab - 11] Real-Time Fraud Detection System
Pro Badge
AI EngineerHQ Challenge
Discord Study Group Sessions
Discord mini-Cohorts and Study Groups
Certified Accreditation
AI EngineerHQ Certified Professional
[Hands-On] Complete the Certification Process
Personalized Mentorship
Scheduling Your 1:1 Sessions
[Hands-On] Project Review with Mentor
[Career Strategy Session] Job applications or freelancing
pitches
Exclusive Industry Access
AI EngineerHQ Job Board
AI Engineering Use Cases Bundle
AI Engineering Case Study Hub
[Hands-On] Crafting a Winning Freelancing Pitch
Advanced Tools and Resources
Azure Credit [In-process]
AI EngineerHQ Toolkit
Live Masterclasses with Industry Experts
Guest Speaker Series
Post-Course Support
Community Access [Discord weekly QnA Calls]
Revenue Generating Projects
Monetizing Your Portfolio [Projects Into SaaS]
About Me
I’m Himanshu Ramchandani, I am from India.
Microsoft MVP
I am an AI Consultant with close to a decade of experience.
I worked on over 100 Data & AI projects in Energy, Healthcare, Law
Enforcement & Defense.
I am the Founder of an AI engineering & Consulting company - Dextar.
I focus on action-oriented AI leadership & engineering implementation
drills.
I provide AI Engineering & Leadership training to teams through AI
Engineer HQ and The Elite [AI Leadership Accelerator]
In the last decade, I have never stopped sharing my knowledge and
have helped over 10000 leaders, professionals, and students.
Detailed Roadmap:
https://god-level-python.notion.site/AI-Engi
neer-HQ-b3c98407b4ab45819811db081ae9d1
02?pvs=4
AI Newsletter:
https://newsletter.himanshuramchandani.co/
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