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9 Months

AI / ML Architect Program

Full-Stack Artificial Intelligence Engineering

A flagship 9-month immersion that builds rock-solid data foundations, levels up your model intuition, and ends with production-ready LLM and agentic systems.

Design resilient data backbones with relational, NoSQL, and optimized SQL workflows.

Build, evaluate, and tune machine learning systems that hold up in real-world product sprints.

Fine-tune large language models and orchestrate agentic AI experiences ready for deployment.

12 Modules
Live Mentorship
Real Projects
Industry Ready

Program Details

Duration
9 Months
Modules
12 Modules
Mentorship
Included
Real-world Projects
Yes
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Program Roadmap

1
Module 1

Database Management Systems

Learners will design efficient databases and write optimized SQL queries.

  • ●Introduction to DBMS and Data Models
  • ●DBMS Architecture (1-tier, 2-tier, 3-tier)
  • ●Entity Relationship (ER) Modeling
  • ●Relational Model and Keys
  • ●SQL Fundamentals (CRUD Operations)
  • ●Joins, Subqueries, Aggregations
  • ●Normalization (1NF, 2NF, 3NF, BCNF)
  • ●Transactions and ACID Properties
  • ●Indexing and Query Optimization
  • ●Introduction to NoSQL Databases
2
Module 2

Python Programming for AI

Students gain strong Python foundations for data analysis and ML workflows.

  • ●Python Syntax and Core Concepts
  • ●Data Structures (List, Tuple, Set, Dictionary)
  • ●Control Flow and Functions
  • ●Object-Oriented Programming (OOP)
  • ●File Handling and Exception Handling
  • ●NumPy for Numerical Computing
  • ●Pandas for Data Analysis
  • ●Data Visualization (Matplotlib, Seaborn)
  • ●Introduction to Scikit-Learn
3
Module 3

Mathematics & Statistics for AI

Learners build mathematical intuition required to understand ML algorithms.

  • ●Linear Algebra (Vectors, Matrices, Dot Products)
  • ●Matrix Operations and Intuition
  • ●Probability Fundamentals
  • ●Random Variables and Distributions
  • ●Mean, Variance, Standard Deviation
  • ●Correlation and Covariance
  • ●Bayes Theorem
  • ●Statistical Intuition for ML Models
4
Module 4

Machine Learning

Students build end-to-end ML pipelines and evaluate models effectively.

  • ●Types of ML (Supervised, Unsupervised, Reinforcement)
  • ●Bias-Variance Tradeoff, Overfitting
  • ●Regression & Classification Algorithms
  • ●K-Nearest Neighbors (KNN), Naive Bayes
  • ●Decision Trees and Random Forest
  • ●Gradient Boosting & XGBoost
  • ●Clustering (K-Means, Hierarchical)
  • ●Dimensionality Reduction (PCA)
  • ●Model Evaluation Metrics
5
Module 5

Deep Learning

Learners understand and implement deep learning models for vision and sequence data.

  • ●Artificial Neural Networks (ANN)
  • ●Activation Functions and Loss Functions
  • ●Gradient Descent and Backpropagation
  • ●Optimization (SGD, Adam) & Regularization
  • ●Convolutional Neural Networks (CNN)
  • ●CNN Architectures (LeNet, VGG, ResNet)
  • ●Recurrent Neural Networks (RNN)
  • ●LSTM, GRU, and Sequence Modeling
6
Module 6

Computer Vision & NLP

Students gain exposure to both vision and language-based AI systems.

  • ●CV: Image Representation & Preprocessing
  • ●CV: Feature Extraction
  • ●CV: CNN-based Vision Pipelines
  • ●NLP: Text Preprocessing, Tokenization
  • ●NLP: Bag of Words, TF-IDF
  • ●NLP: Word Embeddings
  • ●NLP: Introduction to Transformers
7
Module 7

LLMs – From Scratch

Learners deeply understand how modern LLMs like GPT work internally.

  • ●Evolution of Language Models
  • ●Transformer Architecture & Self-Attention
  • ●Positional Encoding & Decoder-Only Models (GPT)
  • ●Tokenization Techniques (BPE)
  • ●LLM Training Pipeline (Conceptual)
8
Module 8

Fine -Tuning LLMs

Students can fine-tune open-source LLMs for custom use cases.

  • ●Pre-training vs Fine-Tuning
  • ●Instruction Tuning
  • ●Parameter-Efficient Fine-Tuning (PEFT)
  • ●LoRA and QLoRA
  • ●Dataset Preparation, Evaluation and Inference
9
Module 9

Agentic AI

Learners design AI agents capable of decision-making and tool interaction.

  • ●Introduction to Agentic AI
  • ●Tool-Using AI Agents
  • ●Planning and Reasoning
  • ●Memory-Enabled Agents
  • ●ReAct Framework & Multi-Agent Systems
10
Module 10

Cloud Computing

Students understand how AI systems are deployed and scaled in the cloud.

  • ●Service Models (IaaS, PaaS, SaaS)
  • ●AWS Core Services (EC2, S3, IAM)
  • ●VMs vs Containers
  • ●Docker Fundamentals
11
Module 11

Big Data & MLOps

Learners build production-ready, maintainable ML systems.

  • ●Big Data Concepts & Hadoop/Spark Overview
  • ●End-to-End ML Lifecycle
  • ●Experiment Tracking & Model Versioning
  • ●MLOps Tools (MLflow, DVC, Git)
12
Module 12

Industry Case Studies

Students develop interview-ready explanations and problem-solving skills.

  • ●Recommendation Systems
  • ●Fraud Detection Systems
  • ●ChatGPT-Style AI Systems
  • ●Healthcare AI Applications
  • ●Autonomous Driving (Perception Layer)
The Complete Journey

From Learning to Production

1 Month Placement Ready Program

ATS-Friendly Resume Building

Resume optimized for MNCs & startups, Keyword-based, HR-approved formats

Mock Interviews (Technical + HR)

Real interview environment, Personalized feedback & improvement plan

Industry Expert Mentorship

Guidance from professionals working in the same field, Insights on hiring trends & expectations

Portfolio Building

Project-based portfolio, GitHub + live project review

LinkedIn Profile Optimization

Recruiter-friendly headline & summary, Job-search & networking strategy

Placement Readiness Support

Interview question bank, Salary negotiation tips, Confidence & communication grooming