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Lead instructor & CTO: Mian Ahmad Basit

Stop building toy demos. Start shipping production AI systems.

Production LLM & Agentic AIEngineering Master Program

Go deep on 37 modules covering LLM fine-tuning, agentic swarms, computer vision, AI security and LLMOps.

A hands-on 7-8 month specialization led by Mian Ahmad Basit. You will fine-tune DeepSeek-R1 and LLaMA 3.1 with QLoRA and DPO, build multi-agent systems with MCP and A2A, ship ColPali vision RAG, add NeMo security guardrails and run it all on Kubernetes.

AI Security & RBAC
AIOps & LLMOps
Vision & VLMs
Fine-Tuning & DPO
MCP & A2A Agents
GraphRAG & Vector

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Your learning path

Python
ML
Deep Learning
Transformers
LLM Fine-Tuning
Computer Vision & VLMs
Multimodal RAG
Agentic AI & MCP/A2A
AI Security & Guardrails
AIOps/LLMOps
Kubernetes Cloud

Modules in 20 phases: 37
Topics and subtopics: 900+
LMS access: 2 years
Alumni: 50,000+

Live weekend sessions with direct mentor guidance

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Short clips from the classroom, student projects and instructor walkthroughs.

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Mian Ahmad Basit
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Success story: Software Engineering with AI
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Success story: Python & Agentic AI Engineer
Mian Ahmad Basit

Learn from practitioners who ship AI for a living

Technical educators and system architects guiding you from Python basics to autonomous multi-agent systems.

Lead technical trainer & curriculum architect

Mian Ahmad Basit

CEO, CTO & Lead AI Engineering Trainer

AI Engineer - GenAI - Agentic AI - Cloud

My mission is to close the gap between toy AI demos and production-grade engineering. As CTO at Pakistan Blockchain Institute and Anz Technologies, founder of P2P Clouds, and mentor to 50,000+ engineers, I guide developers step by step: from Python, linear algebra and PyTorch to fine-tuning LLaMA and DeepSeek, building agentic RAG graphs with LangGraph, mastering MCP and A2A, and deploying secure multi-tenant agent systems on the cloud.

50,000+ trained learners worldwide
50+ live AI SaaS & BaaS products built
CTO @ Pakistan Blockchain Institute
Founder of P2P Clouds & tech lead

ahmadbasit.com

Everything you need to become a production AI engineer

One structured path from Python basics to secure, cloud-deployed agentic systems.

20 learning phases
From Python and ML to advanced agentic AI
900+ topics and subtopics
Complete technical depth, nothing skipped
Production-focused
Evals, guardrails, RAG and observability
AI + ML + DL + GenAI
Complete foundations to state of the art
AWS, GCP, Azure and VPS
Cloud orchestration and multi-cloud deployment
Hands-on projects
Real agentic architectures and systems

What is included

37 modules, 900+ topics
Zero-omission syllabus
Practical GitHub repos
Production-grade code
25+ research papers
Deep research sessions
Live weekend sessions, 8-11 PM
2 years LMS access

Your step-by-step path from zero to senior AI engineer

Pick a phase to see its goals, skills, tools and modules.

Phase 0Weeks 1 - 22 modules

Programming & AI Foundations

Python fundamentals, object-oriented design, async concurrency and the project engineering prerequisites required for AI systems.

Checkpoint

Python Engineering Checkpoint

Milestone

Master Production-Grade Python

Skills you gain

PythonOOPAsyncGit

Tools you use

PythonGitvenvPoetry

Modules in this phase

P0-01Python Syntax & Core Fundamentals
P0-02Object-Oriented & Advanced Python Paradigms

Phase 1 of 20

The complete syllabus

Every topic, protocol and deployment strategy, organised by phase. Search or filter to find exactly what you need.

20
Phases: 37
Modules: 936
Topics & subtopics

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Save every phase, module, topic and project outline to your device.

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Phase 0Weeks 1 - 2 · 2 modules

Programming & AI Foundations

Python fundamentals, object-oriented design, async concurrency and the project engineering prerequisites required for AI systems.

Build production-grade Python foundations covering syntax, data structures, functions, exceptions, file handling, environments, packages and dependency management.

Core Syntax & Control Flow

Python syntax and fundamentals
Variables and data types
Arithmetic, logical and bitwise operators
Conditional statements: if, elif, else
for and while loops
break and continue

Functions & Functional Paradigms

Functions and function arguments
*args and **kwargs
Lambda functions and anonymous execution
Lists, tuples, sets and dictionaries
List, dictionary and set comprehensions
String manipulation
Regular expression parsing

Files, Exceptions & Environments

File handling
Context management
try, except and finally
Modules and packages
Relative imports
venv, Conda and Poetry
pip package management
Dependency locking

Key outcome

Build clean Python projects using production-grade syntax, data structures, packaging and dependency management.

Master advanced Python mechanics including object-oriented programming, iterators, generators, decorators, typing, dataclasses, asynchronous execution, REST APIs and project engineering.

Object-Oriented Programming

Classes and objects mechanics
Inheritance
Multiple inheritance
Method Resolution Order (MRO)
Encapsulation and private attributes
Polymorphism
Duck typing
Abstraction
Abstract Base Classes

Advanced Python Mechanics

Iterator protocol
Custom iterable objects
Generators and yield
Memory-efficient iteration
Decorators and wrapper functions
Stateful decorators
Context managers
__enter__ and __exit__
Type hints
Static typing verification
Dataclasses
Immutable data structures

Async Programming & APIs

asyncio event loop
async and await
Concurrent tasks
asyncio.gather
Semaphores
HTTP and REST fundamentals
Async HTTP requests
JSON serialization and deserialization
.env environment management
Git and GitHub workflows
Python project structure
Package setup

Key outcome

Engineer asynchronous, typed and modular Python services ready for AI workloads.

ML PhaseWeeks 3 - 4 · 2 modules

Machine Learning Foundations

Complete mathematical foundations (linear algebra, calculus, statistics) and classical supervised and unsupervised ML algorithms.

Build the mathematical intuition required to understand optimization, neural networks and modern AI systems.

Linear Algebra

Scalars, vectors, matrices and tensors
Matrix addition and transposition
Dot product
L1 and L2 vector norms
Matrix multiplication
Eigenvalues
Eigenvectors

Probability & Statistics

Probability fundamentals and axioms
Conditional probability
Joint probability
Bayes theorem
Bayesian inference
Discrete and continuous random variables
Gaussian, Bernoulli and Poisson distributions
Mean, variance and standard deviation
Covariance and correlation
Descriptive vs inferential statistics
Sampling techniques
Central Limit Theorem
Hypothesis testing
p-values
Confidence intervals

Calculus & Optimization

Derivatives and rate of change
Partial derivatives
Gradient vectors
Loss surface geometry
Chain rule
Optimization fundamentals
Convexity

Key outcome

Build the mathematical intuition behind optimization and probabilistic modeling used across AI systems.

Master classical machine learning algorithms alongside the feature engineering and evaluation practices required to ship reliable models.

Classical Machine Learning

Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement Learning overview
Linear and multiple regression
Polynomial regression
Logistic regression
KNN
Naive Bayes
Decision Trees
Random Forest
GBM
XGBoost
LightGBM
CatBoost
SVM and SVR
K-Means
Hierarchical clustering
DBSCAN
PCA

ML Engineering & Evaluation

Feature selection
One-Hot Encoding
Target Encoding
Ordinal Encoding
MinMax normalization
Standardization
Missing-value imputation
Outlier detection
Data leakage prevention
Train/validation/test splitting
K-Fold cross-validation
Stratified cross-validation
Grid Search
Random Search
Confusion Matrix
Accuracy, Precision, Recall and F1
ROC-AUC and PR-AUC
MAE, MSE, RMSE and R²
Bias-variance tradeoff
Underfitting and overfitting
L1 and L2 regularization
Scikit-Learn Pipelines
joblib and pickle serialization
ML deployment fundamentals

Key outcome

Train, evaluate and serialize classical ML models with production-grade pipelines.

DL PhaseWeeks 5 - 6 · 2 modules

Deep Learning Foundations

Neural network architectures, forward and backpropagation, loss landscapes, PyTorch mechanics, CNNs and sequence models.

Understand neural network mechanics from perceptrons through backpropagation, optimizers and regularization.

Neural Network Mechanics

Artificial neurons
Perceptrons
Linearity limitations
Forward propagation
Backpropagation
Computation graphs
MSE loss
Cross-Entropy
Binary Cross-Entropy
Gradient Descent
SGD
Mini-batch Gradient Descent
Momentum
Nesterov acceleration
Adam
AdamW
Cosine Annealing
Learning-rate warmup
Xavier initialization
He initialization

Normalization & Regularization

Sigmoid
Tanh
ReLU
Leaky ReLU
GELU
Softmax
BatchNorm
LayerNorm
Dropout
DropPath
L1/L2 neural regularization
Vanishing gradients
Exploding gradients

Key outcome

Trace a neural network from forward propagation through backpropagation and modern optimization.

Engineer models in PyTorch and understand convolutional and recurrent architectures for vision and sequence data.

PyTorch Engineering

PyTorch tensors
Tensor memory layout
Autograd
Custom nn.Module models
Dataset and DataLoader
Training loops
Validation loops
CUDA and MPS acceleration
Mixed precision training
Checkpointing
State Dicts
Transfer learning

CNNs & Sequence Models

Convolution operations
Kernels and filters
Feature maps
Stride and padding
Max and average pooling
ResNet
EfficientNet
Image classification
RNNs
LSTMs
GRUs
Bidirectional RNNs
Seq2Seq
Encoder-decoder architectures
Teacher forcing
Introduction to attention

Key outcome

Build and train CNNs and sequence models in PyTorch, ready for Transformer architectures.

NLP PhaseWeek 7 · 1 module

NLP Foundations

Classical text preprocessing, vector embeddings (Word2Vec, GloVe) and sequence modeling for NLP downstream tasks.

Master classical NLP preprocessing, representation and downstream tasks that lead directly into Transformers.

NLP Foundations

Text normalization
Unicode handling
Tokenization
Stopword removal
Stemming
Lemmatization
N-grams
Bag of Words
TF-IDF
Word2Vec CBOW
Word2Vec Skip-Gram
Negative sampling
GloVe
Contextual embeddings
Text classification
Topic modeling
NER
Sentiment analysis
Intent recognition
Language modeling
Translation
Summarization
Attention in NLP

Key outcome

Build classical NLP pipelines and understand the representations that lead directly into Transformers.

Phase 1Weeks 8 - 9 · 2 modules

AI Foundations & Transformers 101

Deep dive into Transformers, self-attention and full tokenization strategies (BPE, WordPiece, SentencePiece).

Understand the mathematics and implementation of modern Transformer blocks from embeddings through attention and positional encoding.

Embeddings

High-dimensional semantic spaces
Embedding lookup tables
Projection matrices
Cosine similarity
Dot-product similarity

Attention Mechanics

Scaled Dot-Product Attention
Query, Key and Value mathematics
Self-attention step-by-step
Multi-Head Attention
Parallel attention subspaces
Masked causal attention
Cross-attention

Transformer Architectures

Sinusoidal positional encoding
Learned positional encoding
Original Encoder-Decoder Transformer
BERT
RoBERTa
GPT architecture
LLaMA architecture
Encoder-only models
Decoder-only models

Key outcome

Implement and mathematically trace a Transformer attention block from embeddings through attention outputs.

Understand how modern LLM tokenizers convert raw text into token IDs and handle vocabulary construction, subwords and edge cases.

Tokenization Taxonomy

Word-level tokenization
Subword tokenization
Character-level tokenization
Byte-level tokenization

Subword Algorithms

Byte Pair Encoding
BPE algorithm step-by-step
WordPiece
Unigram tokenization
SentencePiece
Byte-level BPE
tiktoken
LLaMA tokenizer

Vocabulary Engineering

Vocabulary construction
Special tokens
Unknown tokens
Padding tokens
Beginning/end-of-sequence tokens
Tokenization edge cases
Token counting
Context-window budgeting

Key outcome

Build and analyze tokenizers while understanding exactly how text becomes model input tokens.

Phase 2Weeks 10 - 11 · 3 modules

Prompts to Programs & Programmatic LLMs

Advanced prompt engineering, Pydantic structured schemas, Outlines FSA grammars and DSPy programmatic pipeline optimization.

Design robust production prompts using systematic prompting strategies, decomposition, structured outputs and iterative refinement.

Prompt Architecture

Anatomy of a production prompt
Zero-shot prompting
One-shot prompting
Few-shot prompting
System prompt design
Role assignment
Output formatting

Reasoning Strategies

Chain-of-Thought prompting
Step-Back prompting
Prompt decomposition
Prompt chaining
Meta-prompting
Self-refinement loops

Prompt Reliability

Prompt sensitivity
Robustness testing
Structured generation
Prompt versioning
Prompt evaluation

Key outcome

Design reliable production prompts instead of relying on ad-hoc prompt experimentation.

Build schema-controlled LLM applications using Pydantic, Instructor and Outlines with validation, retries and constrained decoding.

Instructor & Pydantic

Why typed outputs matter
Instructor client patching
Pydantic execution contracts
Automatic validation retries
Validation-error feedback
Nested schemas
Streaming validation
Async structured outputs
TOOLS mode
JSON_SCHEMA mode
MD_JSON mode

Outlines Guided Generation

Finite State Automata generation
Logit-level token masking
Pydantic schema enforcement
JSON Schema generation
Regex-constrained generation
Context-free grammars
SQL generation
Code generation
Custom DSL generation

Key outcome

Build LLM applications whose outputs conform to machine-verifiable schemas instead of unreliable free-form text.

Replace manual prompt engineering with declarative DSPy programs that automatically optimize instructions, demonstrations and pipeline behavior.

DSPy Core

Prompt engineering vs programmatic LLMs
DSPy signatures
Declarative input/output definitions
DSPy modules
Predict
ChainOfThought
ReAct
ProgramOfThought
LM configuration
Telemetry

DSPy Optimizers

BootstrapFewShot
Automatic few-shot selection
BootstrapFewShotWithRandomSearch
MIPROv2
SIMBA
GEPA
BetterTogether
BootstrapFinetune
Model distillation

Key outcome

Compile an optimized DSPy program instead of manually tuning prompts.

Phase 3Weeks 12 - 13 · 2 modules

KV Cache & Advanced Attentions

Transformer fine-tuning, FlashAttention, Multi-Query Attention (MQA), Grouped-Query Attention (GQA), MLA, PagedAttention and scaling laws.

Implement Transformer attention components and fine-tune encoder, decoder and encoder-decoder architectures on custom datasets.

Transformer Fine-Tuning

Coding attention mechanisms in PyTorch
DistilBERT classification fine-tuning
Custom classification datasets
DistilGPT generation fine-tuning
Custom generation datasets
T5 Seq2Seq fine-tuning
Custom sequence-to-sequence datasets

Key outcome

Fine-tune encoder, decoder and encoder-decoder Transformer architectures on custom tasks.

Understand the memory and compute bottlenecks of autoregressive decoding and the modern attention mechanisms used to solve them.

KV Cache & Efficient Attention

Naive decoding and O(N²) redundant computation
KV Cache mechanics
KV Cache memory calculations
FlashAttention 1
FlashAttention 2
FlashAttention 3
IO-aware GPU tiling
PyTorch SDPA

Attention Variants

MHA memory bottleneck
Multi-Query Attention
Grouped-Query Attention
GQA in LLaMA-3
Multi-Head Latent Attention
MLA in DeepSeek models
PagedAttention
vLLM virtual memory management
RoPE

Scaling Laws

Kaplan scaling laws
Chinchilla compute-optimal scaling
Tokens-to-parameters ratio
Dataset scaling mathematics
Compute budget allocation

Key outcome

Analyze and optimize LLM attention memory, throughput and compute requirements for production inference.

Phase 4Weeks 14 - 16 · 3 modules

Finetuning Language Models & PEFT

Pre-training lifecycle, SFT, LoRA, QLoRA, DoRA, preference alignment (DPO, GRPO, ORPO), quantization and synthetic datasets.

Understand the complete model lifecycle from pre-training and continued pre-training through instruction tuning and synthetic dataset engineering.

Training Lifecycle

Pre-training vs post-training
Base model capabilities
Base model limitations
Causal Language Modeling
Masked Language Modeling
Prefix Language Modeling
Continued Pre-Training
Domain adaptation
Compute budget tradeoffs
When CPT is appropriate
Multi-Token Prediction

Data Preparation

CommonCrawl
RefinedWeb
Instruction-pair datasets
Chat-format datasets
ChatML templates
LLaMA-3 chat format
Mistral formatting
Loss masking
Dataset deduplication
Filtering pipelines

Synthetic Data

Self-Instruct
Alpaca-style datasets
Synthetic instruction generation
Data quality filtering

Key outcome

Engineer high-quality datasets and understand exactly where CPT, SFT and synthetic data fit into the model lifecycle.

Master adapter-based fine-tuning techniques and the mathematics behind low-rank adaptation and quantized training.

PEFT Mathematics

Intrinsic dimensionality
LoRA mathematics
W = W0 + BA
Rank selection
Alpha scaling
QLoRA
NF4 quantization
Double quantization
Paged optimizers
DoRA
Magnitude and direction decomposition
AdaLoRA
Adaptive rank allocation
LoRA+
Separate A/B learning rates

Supervised Fine-Tuning

SFT pipeline design
Instruction tuning
FLAN datasets
Alpaca datasets
OpenHermes datasets
Chat fine-tuning
Conversational fine-tuning
Reasoning fine-tuning
Domain-specific fine-tuning

Key outcome

Fine-tune an open-weight LLM efficiently using QLoRA and production-grade SFT datasets.

Master modern post-training methods for aligning language models with human and programmatic preferences while preparing models for efficient deployment.

Preference Alignment

Why SFT alone is insufficient
Distribution shift
Verbosity bias
RLHF
PPO
Reward models
Critic networks
KL penalties
DPO
GRPO
ORPO

Quantization & Inference Preparation

GPTQ
AWQ
BNB NF4
FP8
LoRA adapter merging
Multi-LoRA serving
vLLM
SGLang
GGUF
GGUML
llama.cpp
Speculative decoding

Training Tooling

Hugging Face TRL
SFTTrainer
DPOTrainer
GRPOTrainer
ORPOTrainer
Unsloth
Axolotl
LLaMA-Factory
Together AI
AWS SageMaker fine-tuning

Synthetic Preference Data

Instruction synthetic data
Preference synthetic data
Code synthetic data
LLM-as-Judge scoring
distilabel
DataDreamer
Argilla
Model collapse risks
Data poisoning prevention
End-to-end SFT dataset pipeline

Key outcome

Align, quantize and prepare an open-weight LLM for efficient production inference.

Phase 5Week 17 · 2 modules

Mixture of Experts & Reasoning Models

Mixture of Experts (MoE) routing mechanisms, DeepSeek-R1-Zero reasoning models and chain-of-thought reinforcement learning.

Understand sparse expert architectures used by modern production models.

Mixture of Experts

Dense-model scaling limitations
Sparse expert activation
Gating networks
Top-K routing
Router mathematics
Load-balancing loss
Auxiliary losses
Expert capacity
MoE training at scale
MoE inference
Memory vs compute tradeoffs
Mixtral 8x7B
DeepSeek-V3 256 experts
Production MoE models
MoE vs dense architectures

Key outcome

Explain how sparse expert routing enables larger models without proportional compute cost.

Understand reinforcement-learning approaches used by modern reasoning models.

Reasoning Models

Reasoning model concept
System 1 vs System 2
Chain-of-Thought
Pure RL reasoning
SFT + RL reasoning
DeepSeek-R1-Zero
Rule-based rewards
GRPO reasoning
Reasoning trace distillation

Key outcome

Explain how reinforcement learning produces modern reasoning-capable models.

Phase 6Week 18 · 2 modules

Knowledge Distillation & Small Language Models

SLM design philosophy, Liquid Foundation Models, soft labels, KL divergence and student-teacher pipeline construction.

Explore SLM design philosophy, edge deployment and training/fine-tuning of small models.

Small Language Models

SLM design philosophy
Phi-3
Gemma-2B
LLaMA-3.2-1B
Latency optimization
Privacy advantages
Edge deployment
Data density
Liquid Foundation Models
Dynamical systems
Training SLMs from scratch
SLM fine-tuning
Reasoning trace distillation
SLM vs LLM tradeoffs

Key outcome

Design and deploy compact, edge-ready small language models.

Compress foundation-model capabilities into smaller student models.

Knowledge Distillation

Teacher-student paradigm
Hard labels
Soft labels
Temperature scaling
KL divergence
Attention transfer
Hidden-state matching
Teacher logit generation
Student architecture design
Student training pipeline

Key outcome

Build a teacher-student distillation pipeline that compresses a large model into a compact one.

Phase 7Week 19 · 2 modules

Multimodal Vision Systems & Image Understanding

Vision Transformers (ViT), CLIP, SigLIP, DINOv2 and Visual Language Models (VLMs) like LLaVA and Qwen-2-VL.

Bridge from CNNs to Vision Transformers and modern visual encoders.

Vision Transformers

CNN-to-ViT conceptual bridge
ViT architecture
16x16 image patches
Patch embeddings
CLS token
2D positional encoding
Visual self-attention
Attention maps
CNN vs ViT inductive bias
CLIP
SigLIP
DINO
DINOv2

Key outcome

Implement and reason about Vision Transformer patch embeddings and self-attention.

Understand how visual encoders connect to LLM backbones to produce multimodal reasoning.

Visual Language Models

VLM architecture
LLaVA
Qwen-2-VL
Gemini Vision
Visual encoder
ViT/SigLIP encoders
MLP projectors
Cross-attention aligners
Perceiver alignment
Vision-language feature spaces
Visual grounding

Key outcome

Explain and build the encoder-projector-LLM pipeline behind modern VLMs.

Phase 8Week 20 · 1 module

Speech AI & Conversational Audio Systems

Speech-to-Text (STT) foundations, Whisper architecture, acoustic dataset preparation and custom fine-tuning.

Understand speech signal processing and build custom speech-to-text pipelines with Whisper.

Speech AI

Speech signal processing
Log-Mel spectrograms
Speech-to-Text
Whisper API
Whisper architecture
Audio Encoder-Decoder Transformer
Streaming chunking
Audio dataset preparation
Whisper fine-tuning
Domain accents
Domain-specific audio

Key outcome

Fine-tune Whisper on domain-specific audio and build streaming speech-to-text systems.

Phase 9Week 21 · 1 module

Representation Learning & Embedding Systems

Dense, sparse, quantized, binary and multi-vector (ColBERT) embeddings, Matryoshka Representation Learning (MRL) and embedding fine-tuning.

Understand modern embedding architectures from dense vectors through sparse lexical signals, quantized representations, ColBERT multi-vectors and Matryoshka embeddings.

Embedding Taxonomy

Dense embeddings
BGE
E5
OpenAI text-embedding models
Sparse embeddings
SPLADE
BM25
Quantized embeddings
Float32 to Int8
Uint8 compression
Binary embeddings
Hamming distance
Multi-vector embeddings
ColBERT
ColPali

Matryoshka Representation Learning

MRL concept
Flexible embedding dimensions
Nested dimension loss
1536 to 256 dimension retrieval
Dynamic query-time dimensions

Embedding Fine-Tuning & Evaluation

Multiple Negatives Ranking Loss
Embedding fine-tuning
Domain adaptation
MTEB benchmark
Embedding leaderboard analysis
State-of-the-art embedding evaluation

Key outcome

Select, fine-tune, compress and benchmark embeddings for enterprise retrieval workloads.

Phase 10Weeks 22 - 24 · 3 modules

Production RAG Systems

LangChain 2026, LlamaIndex, Hybrid RAG, Self-RAG, Corrective RAG, Adaptive RAG, ColPali multimodal RAG, GraphRAG and RAG security.

Build production retrieval pipelines using LangChain and LlamaIndex with document loaders, vector stores, metadata filtering and query engines.

LangChain Ecosystem

LangChain architecture
LLM integrations
Model abstraction
LCEL
Structured outputs
Output parsers
Tool calling
Function calling
Conversation memory
Document loaders
Text splitters
Embeddings
PGVector
Pinecone
Qdrant
Retrieval pipelines
Text-to-SQL
Callbacks
LangSmith telemetry
Multimodal integrations
Deep Agents

LlamaIndex Ecosystem

Documents
Nodes
Chunking strategies
PDF/API/web/filesystem loaders
Embedding configuration
Chroma
FAISS
Pinecone
Qdrant
Vector Index
Summary Index
Keyword Index
Retrievers
Query engines
Metadata filtering
Hybrid search
Reranking
Response synthesis

Key outcome

Build production RAG applications using both major Python retrieval ecosystems.

Move beyond vanilla RAG into sophisticated retrieval architectures with query transformation, reranking, reflection and vector compression.

RAG Foundations

Vanilla RAG pipeline
RAG limitations
Embedding model selection
Fixed chunking
Sentence chunking
Recursive chunking
Semantic chunking

Advanced Retrieval

BM25
SPLADE
ColBERT
Hybrid dense + sparse retrieval
Reciprocal Rank Fusion
Meta Hybrid RAG
Query rewriting
Sub-query decomposition
HyDE
Contextual retrieval
Anthropic contextual chunking
Agentic RAG

Self-Reflective RAG

RAG evaluation
Context precision
Context recall
Faithfulness
Answer relevance
Cohere Rerank
BGE-Reranker-v2
Cross-encoder reranking
Self-RAG
Corrective RAG
Fallback web search
Adaptive RAG
Complexity-based routing

Vector Quantization

Scalar Quantization
SQ8
Binary Quantization
BQ
Product Quantization
PQ

Key outcome

Design retrieval architectures that dynamically select, transform, rerank and validate information.

Build enterprise RAG systems capable of understanding complex documents, knowledge graphs, multimodal content and security boundaries.

Multimodal Document RAG

ColPali visual page retrieval
Single-stage parsing
Dual-stage parsing
Layout detection
OCR
Structured layout chunking
Vision-language embeddings
Vision-language rerankers
Multimodal LLM document understanding
AWS Textract
Azure AI Document Intelligence
Google Cloud Document AI
LlamaParse
Docling
LiteParse

Graph RAG & Vectorless RAG

Neo4j GraphRAG
Knowledge graph construction
Entity extraction
Relation extraction
Graph traversal
Vectorless RAG
PageIndex

Caching & Security

Redis caching
GPTCache
Semantic caching
PII masking
Anonymization pipelines
Input guardrails
Output guardrails
Prompt injection defense
Retrieval-interface security

Key outcome

Ship a secure multimodal and graph-enhanced enterprise RAG system with caching and access controls.

Phase 11Weeks 25 - 26 · 2 modules

Agentic Systems: Core Architectures & Foundations

Pydantic validation, LangGraph stateful graphs, PydanticAI type-safe agents, Pydantic Evals, MCP and A2A protocols.

Build reliable stateful agents using Pydantic contracts and LangGraph graph-based orchestration.

Pydantic Validation

Pydantic installation
Models
Fields
Validators
JSON parsing
Type coercion
Dataclasses
Nested models
Validation decorators
Custom validators
Settings management

LangGraph Architecture

LangGraph foundations
StateGraph
START and END
State management
Reducers
Nodes
Edges
Conditional routing
Tool calling
ReAct loops

Agent State & Memory

Human-in-the-loop interrupts
Approval workflows
Edit State
Checkpointers
Postgres persistence
MemorySaver
Subgraphs
Supervisor architectures
Multi-agent swarms
Streaming
Observability
LangGraph Platform

Key outcome

Build a stateful, checkpointed LangGraph agent with tool execution and human approval workflows.

Engineer type-safe agents using PydanticAI with structured outputs, dependency injection, retries, evaluations and tracing.

PydanticAI Architecture

PydanticAI architecture
Agents
Models
Runnable contracts
Provider configuration
Fallback models
Result types
Pydantic structured results
Union result types
Discriminated unions

Agent Tools & Validation

Field validators
Model validators
Static system prompts
Dynamic system prompts
Instruction patterns
Dependency injection
deps_type
RunContext
Type-safe context
@agent.tool
RunContext tools
@agent.tool_plain
ModelRetry
Validation error handling

Streaming & Multi-Agent Systems

stream_text
stream
astream_events
Multi-turn conversations
Message history
Context window management
Agent pipelines
Delegation
Handoffs
TestModel
FunctionModel
Agent unit testing

Evals & Observability

Pydantic Evals
Datasets
Evaluation cases
Experiment model
Built-in evaluators
Custom evaluators
LLM-as-a-Judge
Online evaluation
Logfire integration
Tracing
Metrics
Cost monitoring
FastAPI deployment
Sessions
Containerization

Key outcome

Build type-safe, testable agents with automated evaluation and production-grade observability.

Phase 12Weeks 27 - 28 · 2 modules

Agents: Orchestration, Protocols & Production

Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A), function calling mechanics and agent observability tools.

Master the standardized protocol for connecting agents to tools and resources across distributed systems.

Model Context Protocol

Why MCP exists
Tool fragmentation problem
MCP Hosts
MCP Clients
MCP Servers
MCP Tools
MCP Resources
MCP Prompts
stdio transport
SSE transport
FastMCP server development
MCP client development
Remote MCP servers
MCP authentication
MCP authorization
Trust boundaries
Multi-server orchestration
MCP with LangGraph
MCP with Claude

Key outcome

Build and connect MCP servers and clients across a multi-tool agent system.

Master agent interoperability and robust function execution alongside end-to-end observability of agent workflows.

Agent-to-Agent Protocol

Why A2A exists
Agent interoperability
Agent Cards
Agent discovery
/.well-known/agent.json
A2A task lifecycle
Submitted state
Working state
Completed state
Failed state
Streaming states
HTTP transport
SSE transport
JSON-RPC messages
A2A server
A2A client
Remote agent delegation
A2A vs MCP

Function Calling

LLM-to-tool communication
JSON Schema tool definitions
Parameter typing
Tool descriptions
Function calling lifecycle
Parallel tool calls
Forced tool selection
Required tool choice
No-tool choice
Specific tool selection
Structured output vs function calling
Tool executor loops

Agent Observability

LangSmith tracing
LangSmith span telemetry
Dataset annotation
Logfire tracing
Span-level telemetry
Langfuse monitoring
Token monitoring
Cost monitoring
Latency monitoring
Multi-agent tracing
Tool execution traces
Agent handoff tracing
Failure analysis
Cost attribution
Latency analysis
Protocol-level observability

Key outcome

Connect multiple agents through A2A, execute robust function calls and trace multi-agent workflows end to end.

Phase 13Weeks 29 - 30 · 2 modules

Context Engineering & Harness Engineering

Anatomy of context windows, context compression (LLMLingua, RECOMP), evaluation harnesses (lm-eval, Inspect AI) and E2B sandboxes.

Engineer the context supplied to LLMs and agents using retrieval, memory, structured tool results and compression.

Context Construction

Anatomy of the context window
System instructions
Conversation history
Retrieved context
Tool results
Structured data
Output constraints
RAG as context construction
Chunking impact on context quality

Memory Architectures

Short-term memory
Episodic memory
Semantic memory
Procedural memory
Conversation state
Context accumulation

Context Compression

Context pruning
Context compression
LLMLingua
RECOMP
Long-context optimization
Tool-result compression
Retrieval context selection

Key outcome

Construct compact, relevant and high-signal context for long-running AI agents.

Build automated evaluation harnesses and secure execution environments for tool-using and coding agents.

Evaluation Harnesses

Harness engineering
Why ad-hoc testing fails
Evaluation harness fundamentals
lm-evaluation-harness
MMLU
GSM8K
TruthfulQA
Inspect AI
Task abstraction
Solver abstraction
Scorer abstraction
LLM-as-a-Judge
Judge calibration
Evaluation bias mitigation

Secure Execution

E2B sandboxes
Modal sandboxes
Docker code execution
Secure tool execution
Agent checkpointing
LangGraph persistence
Interrupts

ReAct Safety Harnesses

Iteration guards
Fallback handlers
Token budgets
Execution time limits
Tool failure handling
Supervisor-worker contracts
Multi-agent execution harnesses

Key outcome

Evaluate agents automatically and execute untrusted agent-generated code inside controlled sandboxes.

Phase 14Week 31 · 1 module

LLMOps & Observability

Span-level tracing, LangSmith, Langfuse, Logfire, RAGAS evals, prompt caching, semantic Redis caching and token budgeting.

Operate production AI systems using tracing, evaluation, token accounting, caching, rate limiting and financial controls.

Tracing & Evaluation

LangSmith tracing
Span telemetry
Langfuse token monitoring
Langfuse latency monitoring
Logfire span-level traces
RAGAS evaluation
Inspect AI evaluation
RAG validation
Agent validation

Caching & Cost Control

Prompt caching
Anthropic prompt prefix caching
OpenAI prompt caching
Semantic caching
Redis caching
GPTCache
Token accounting
Rate limiting
Token budgets
Financial budget enforcement

Key outcome

Instrument production AI workloads with end-to-end telemetry, evaluation and automated cost controls.

Phase 15Weeks 32 - 33 · 1 module

Production Infrastructure & Cloud Deployment

Inference serving (vLLM, SGLang, llama.cpp), speculative decoding, Kubernetes (EKS), Docker, Helm and GitHub Actions CI/CD.

Deploy high-throughput model-serving infrastructure using modern inference engines, Kubernetes and cloud-native CI/CD.

Inference Serving

vLLM
SGLang
llama.cpp
High-throughput inference
Dynamic LoRA loading
Multi-adapter serving
Speculative decoding
Quantized inference
GGUF
AWQ
GPTQ
Continuous batching

Cloud Infrastructure

AWS EKS
AWS ECR
AWS Batch
ECS Fargate
Kubernetes clusters
Helm charts
Cloud-native AI architecture

Containerization & CI/CD

Docker
Multi-stage Docker builds
Lightweight AI containers
GitHub Actions
AI application CI/CD
Automated deployments
Production configuration management

Key outcome

Deploy a scalable LLM inference platform on Kubernetes with automated container builds and CI/CD.

Phase 16Week 34 · 1 module

AI Security, Guardrails & RBAC

Prompt injection defense, NVIDIA NeMo Guardrails, LlamaFirewall, Presidio PII, LiteLLM gateways and retrieval/prompt RBAC.

Secure enterprise AI applications against prompt injection, jailbreaks, data poisoning, PII exposure and unauthorized information access.

Guardrails & Defense

Direct prompt injection
Indirect prompt injection
Jailbreak prevention
PII filtering
Toxicity filtering
Output validation
NVIDIA NeMo Guardrails
Colang
LlamaFirewall
LLM Guard
AWS Bedrock Guardrails
Microsoft Presidio
PII detection
PII redaction
Data poisoning defense

LLM Gateways & Routing

LLM gateway architecture
Load balancing
Automatic retries
Gateway metadata
Gateway observability
Gateway caching
LiteLLM Proxy
AWS Bedrock routing
Provider routing
Model fallback strategies

Identity, RBAC & Enterprise Security

Identity fundamentals
Authentication
Authorization
SAML assertions
JWT
RBAC
Retrieval-layer RBAC
Document-level tenant filtering
Prompt-layer RBAC
Dynamic system prompts by role
Multi-tenancy
Tenant isolation
Data isolation guarantees

Security Evaluation

RAG security evaluation
Agent security evaluation
Prompt injection stress testing
Unauthorized retrieval testing
PII leakage testing
Tool authorization testing
Multi-tenant isolation testing

Key outcome

Deploy enterprise AI behind guardrails, secure gateways, PII protection and tenant-aware RBAC controls.

Lead instructor, CTO @ PBI & Anz Tech

Direct mentorship, guaranteed

“We don't teach surface-level prompts. You will write production Python code, build n8n workflows, deploy open-weight DeepSeek-R1 models, and configure NVIDIA security guardrails.”

Mian Ahmad Basit

Learn the official stack used by top AI labs and enterprises

No toy libraries. You get hands-on practice with n8n, Claude, OpenAI, NVIDIA NeMo, Hugging Face, DeepSeek and Meta LLaMA.

n8n
Workflow automation
Self-hosted AI
Enterprise AI agent workflow orchestration, webhooks and FastMCP nodes
Claude
Anthropic AI
Computer Use & FastMCP
Artifacts, Computer Use, prompt caching and high-reasoning agent execution
OpenAI ChatGPT
Frontier LLM
Realtime & function calling
Structured JSON outputs, Assistants API, voice realtime and o1/o3 reasoning
NVIDIA NeMo & NIM
Enterprise security & acceleration
NeMo Guardrails & NIM
Prompt injection defense, PII masking, TensorRT-LLM microservices
Hugging Face
Open-source ecosystem
Transformers & TGI
Model Hub, datasets, Text Generation Inference, PEFT and safetensors
DeepSeek-R1 & V3
Reasoning & MoE
Chain-of-thought (CoT)
Multi-head Latent Attention (MLA), GRPO rule-based RL and self-hosted distillation
Meta LLaMA 3.3
Open weights
8B / 70B / 405B
QLoRA 4-bit, DPO alignment, Unsloth acceleration and on-prem deployment
Google Gemini
Multimodal frontier
2M token context
Context caching, ColPali vision RAG and video document processing
LangGraph & LangChain
Agent framework
Stateful swarms
Human-in-the-loop interrupts, state checkpointing and A2A routing
Qdrant & Pinecone
Vector database
HNSW & ColBERT
High-density vector indexing, hybrid BM25 retrieval and payload filtering
vLLM & Ollama
LLMOps serving
PagedAttention
Continuous batching, LoRA multi-serving and local on-prem cluster execution
FastMCP & Docker/K8s
Protocol & cloud infrastructure
Helm & Kubernetes
Model Context Protocol, containerized microservices and auto-scaling pods

Models, providers and automation

Frontier models & providers

API and open-weights integration

OpenAI
GPT-4o, o1, o3-mini, Realtime API and function calling
Claude (Anthropic)
Claude 3.5 Sonnet, Artifacts, Computer Use and prompt caching
NVIDIA
NeMo Guardrails, NIM microservices and TensorRT-LLM
Hugging Face
Transformers, datasets, TGI, Hub models and PEFT
DeepSeek
DeepSeek-R1, V3, reasoning chains and distillation
Meta AI
LLaMA 3.1 / 3.3 (8B, 70B, 405B) fine-tuning
Google Gemini
Gemini 1.5 Pro/Flash, context caching and multimodal RAG

Workflow & agent automation

Low-code to code-native platforms

n8n
Self-hosted enterprise AI workflow orchestration and webhooks
FastMCP
Model Context Protocol tooling and server architecture
LangGraph
Stateful multi-agent swarms with human-in-the-loop
PydanticAI
Type-safe production agent framework in pure Python
CrewAI / AutoGen
Role-based multi-agent collaboration networks
Ollama / vLLM
Local and cloud high-throughput inference engines

See how AI swarms and enterprise RAG pipelines run

Run the live trace to watch guardrails, tool calls and multi-agent protocols working together.

Multi-agent swarms & MCPColPali vision & GraphRAGAI security & NeMo GuardrailsvLLM serving & Kubernetes

Live architecture engine v2.4

ACTIVE

Test prompt

Tokens: ~24

Pipeline trace logs

> [System Init] Loading LangGraph Supervisor with FastMCP tools...

> [Security Gate] NVIDIA NeMo Guardrail: Input Sanitized (No PII / No Injection Detected)

> [Agent Router] Request dispatched to DevOps Agent & Code Review Agent via A2A Protocol

> [Tool Call] FastMCP Server invoked: kubectl get pods -n production

> [Reasoning Trace] DeepSeek-R1 chain-of-thought: Identifying memory bottleneck on worker node 04...

> [Human-in-the-Loop] HITL interrupt triggered for write permission: Patch deployment approval required

> [Status] Agent Execution Paused - Waiting for Human Operator Checkpoint

01NeMo security gate
PII masking with Presidio, prompt injection defense and input policy validation.
02LangGraph supervisor
Stateful graph routing, checkpoint persistence and human-in-the-loop interrupts.
03FastMCP & A2A tools
Standardized Model Context Protocol servers and inter-agent delegation.
04vLLM & Inspect AI
High-throughput model serving with LoRA adapter routing and eval-gated score checks.

Want to build these architectures from scratch?

Get the full syllabus and join the Master Cohort.

Build zero-trust security into production AI

Enterprise clients will not buy unsafe AI. Learn NeMo Guardrails, Presidio PII masking, prompt injection defense and air-gapped private VPCs.

Input, output and dialog rules
NVIDIA NeMo Guardrails
Prevent hallucination, enforce strict brand dialog boundaries and block prompt injection attempts in real time.
Data redaction and anonymization
Microsoft Presidio PII masking
Automatically strip social security numbers, credit cards, emails and sensitive credentials before they reach LLM APIs.
Jailbreak and attack hardening
OWASP Top 10 for LLMs defense
Protect against indirect prompt injection, vector database denial-of-service, data poisoning and unauthorized tool calls.
On-prem DeepSeek & LLaMA
Private air-gapped VPC deployment
Deploy zero-data-leakage open-weight models inside local Kubernetes clusters or isolated AWS/GCP VPCs.

Covered in Phase 10 (RAG security) and Phase 16 (AI security, guardrails and RBAC)

The security promise

“I teach the exact zero-leakage security patterns required by banks, health networks and Fortune 500 enterprises.”

Master 60+ modern AI frameworks and tools

From agent orchestrators and coding assistants to protocols, memory systems, observability and cloud environments.

Agent Frameworks

LangChain
Core
Ecosystem for LLM application components and LCEL pipelines.
LangGraph
Core
Stateful, cyclic multi-agent graph orchestration.
OpenAI Agents SDK
Core
Official OpenAI SDK for agent loops and tool execution.
Google ADK
Core
Google Agent Development Kit for Gemini and Vertex AI.
AWS Strands
Core
AWS framework for building cloud-native multi-agent swarms.
CrewAI
Core
Role-based multi-agent orchestration framework.
LlamaIndex
Core
Data framework for LLMs, RAG and query workflows.
n8n
Visual node-based workflow automation for AI tools.
LangFlow
Interactive visual UI for building and testing AI agents.

Coding Agents & IDEs

Claude Code
Core
Anthropic agentic CLI for workspace code refactoring.
OpenClaw
Open-source autonomous coding agent.
NemoClaw
NVIDIA NeMo backed coding assistant.
Hermes Agent
Core
Autonomous agent CLI built for persistent terminal execution.
Codex CLI
Command-line interface for OpenAI code generation.
opencode
Open terminal coding agent for automated repositories.
Antigravity IDE
Core
Agentic AI coding workspace powered by Gemini.

Protocols & Standards

MCP (Model Context Protocol)
Core
Open standard for connecting AI models to tools and data sources.
A2A (Agent-to-Agent Protocol)
Core
Standardized protocol for inter-agent task delegation and discovery.

Memory & Knowledge

mem0
Core
Universal long-term memory layer for AI agents and assistants.
LangGraph Checkpointing
Core
State persistence and time-travel debugging in graph agents.
LlamaIndex Memory
Contextual memory modules for conversational query engines.
GraphRAG (Neo4j)
Core
Knowledge graph powered retrieval augmented generation.

AgentOps & Observability

AgentOps
Core
Agent benchmarking, cost analysis and execution tracking.
LangSmith
Core
Full-suite tracing, debugging and testing platform.
Opik
Open-source LLM evaluation and monitoring engine.
Langfuse
Core
Open-source observability, prompt management and analytics.
Logfire
Core
Span-level telemetry from the Pydantic team.

Core AI Concepts

RAG
Core
Retrieval Augmented Generation for external knowledge grounding.
Agentic RAG
Core
Agent-driven dynamic search, self-correction and synthesis.
Context Engineering
Core
Optimizing context windows, pruning and memory layout.
Prompt Engineering
Core
CoT, few-shot and structured prompt optimization.
Pydantic
Core
Data validation and typed schemas for LLM contracts.
Vector DBs (Qdrant/Pinecone)
Core
High-speed vector similarity indices.
Agent Evaluation (Inspect AI)
Core
Task, solver and scorer evaluation harnesses.
Agent Security & RBAC
Core
Multi-tenant isolation, prompt defense and JWT RBAC.
Guardrails (NeMo / Presidio)
Core
Input/output scanners, toxicity filters and PII masking.
FAISS
Facebook AI Similarity Search vector library.
Tavily
Core
Search API optimized for LLM agent research loops.

Deployment & Web UI

Docker
Core
Multi-stage containerization for reproducible builds.
GitHub Actions
Core
Automated CI/CD workflows for testing and deployment.
FastAPI
Core
High-performance Python web framework for async AI APIs.
BentoML
Unified framework for serving and packaging ML models.
Nginx
High-performance reverse proxy and SSL termination.
Streamlit
Core
Rapid Python web UI prototyping for data and AI.
Gradio
Interactive Python interfaces for machine learning models.

Cloud Infrastructure

AWS (EKS / Bedrock / Lambda)
Core
Amazon Web Services cloud suite for AI workloads.
GCP (Vertex AI / Cloud Run / GKE)
Core
Google Cloud Platform AI and serverless containers.
Azure (Azure OpenAI / AI Foundry)
Core
Microsoft Azure enterprise cloud platform.
Self-Hosted VPS
Core
Bare-metal and private virtual servers for cost efficiency.

Model Providers & Inference

OpenAI (GPT-4o / o3-mini)
Core
Frontier models and structured output endpoints.
Anthropic (Claude 3.5 Sonnet)
Core
High intelligence reasoning and coding models.
Google Gemini (1.5 Pro / Flash)
Core
Multimodal 2M+ token context window models.
Groq
Core
Ultra-fast LPU inference engine for open-source LLMs.
Ollama
Core
Local LLM runner for LLaMA-3, DeepSeek-R1 and Phi-3.
AWS Bedrock
Core
Managed foundation models on Amazon Web Services.
Azure OpenAI
Core
Enterprise-grade managed OpenAI endpoints on Azure.

Career scope and earning potential

AI engineering is one of the most in-demand technical skill sets. These ranges show what skilled engineers can charge.

Full-time jobs
Global AI engineer salary
$120K - $250K / yr
Top remote positions across US, UK, EU and UAE companies seeking specialized agentic and RAG engineers.
Upwork & LinkedIn
Remote consulting rate
$80 - $250 / hr
High-ticket hourly rates for custom DeepSeek fine-tuning, vector database optimization and agent setups.
Per-project contracts
Enterprise AI agency retainer
$5,000 - $25,000
Building multi-tenant AI SaaS, self-hosted n8n automation and confidential enterprise RAG pipelines.

Freelancing income estimator

Estimate your monthly revenue from part-time remote consulting.

Hourly rate$120/hr

$50 junior$250 architect

Hours per week15hrs/wk

5 hrs side hustle40 hrs full-time

Estimated monthly earnings

$7,200/ month

About $86,400 per year from part-time remote consulting.

Illustrative market ranges, not income guarantees. Your results depend on your skills, portfolio and effort.

Student bonuses and career accelerator toolkit

Beyond the technical curriculum, enrollment includes career placement support, freelancing frameworks, LMS access and direct mentorship.

Hands-on practical projects
Production repos & full codebases
Zero fluff or toy demos. You build production-grade, containerized LLM and agent systems on AWS, GCP and Kubernetes.
- Production GitHub repositories with complete test suites
- Dockerized FastAPI microservices and Helm charts
- DeepSeek-R1 and LLaMA 3.1 QLoRA/DPO fine-tuning notebooks
- ColPali VLM multimodal RAG and Neo4j knowledge graph setups
Included in the program
LMS recorded lectures
2 years of LMS portal access
Never miss a single insight. Revisit HD class recordings, code repositories and slide decks anytime for 24 months.
- Searchable HD session recordings with timestamps
- Downloadable Jupyter notebooks and architecture PDF blueprints
- Interactive module quizzes and self-assessment benchmarks
- Quarterly content updates on new AI breakthroughs
Included in the program
Internship opportunities
AI engineering internships & career growth
Fast-track your career with internship referrals at partner organizations, Pakistan Blockchain Institute and Anz Technologies.
- Internship placement path for top program performers
- Official verified certificate of completion from the instructor
- 1-on-1 portfolio and GitHub profile review by Mian Ahmad Basit
- Mock technical interviews for AI engineer and LLMOps roles
Included in the program
Freelancing & business development
AI agency & freelance business playbook
Learn how to monetize your skills, win high-ticket $5,000-$25,000 AI consulting clients and launch scalable AI SaaS products.
- Proven Upwork and LinkedIn client acquisition scripts
- Enterprise AI architecture proposal and scope templates
- Pricing models for custom LLM fine-tuning and agentic automation
- Building multi-tenant AI SaaS/BaaS products from scratch
Included in the program

Direct guidance by Mian Ahmad Basit

CTO @ Pakistan Blockchain Institute & Anz Technologies, founder of P2P Clouds

“I review code repos, guide business pitches and personally mentor candidates aiming for senior AI roles.”

Where do you stand in your AI journey?

Answer 3 quick questions to find your personalized entry point.

Question 1 of 3

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What is your current background in Python and programming?

Frequently asked questions

Clear answers about the program structure, prerequisites, tools and support.

Python developers, software engineers, AI/ML engineers, data scientists, full-stack developers and technical professionals moving into modern production AI.
If you want to move beyond surface-level prompting and build production-grade agentic systems, fine-tuned LLMs and cloud-native AI pipelines, this program is designed for you.
The lead instructor is Mian Ahmad Basit, supported by experienced engineers from the faculty.
No. The roadmap starts with Python, math, ML, deep learning and NLP foundations before moving into transformers, LLM fine-tuning, RAG, agents, security and cloud deployment.
Yes. The program focuses on production Python, FastAPI services, Dockerized systems, vector databases, agent orchestration, guardrails, observability and cloud deployment patterns.
OpenAI GPT-4o and o-series models, Claude 3.5 Sonnet, DeepSeek-R1 and V3, Meta LLaMA 3.1/3.3, Google Gemini, Hugging Face models, Ollama and vLLM.
Yes. You cover hybrid RAG, Self-RAG, Corrective RAG, Adaptive RAG, ColPali multimodal RAG, Neo4j GraphRAG, vector search, reranking, retrieval security and evaluation.
Yes. The curriculum includes LangGraph, PydanticAI, MCP, A2A protocols, tool calling, supervisor agents, state checkpointing, human-in-the-loop workflows and agent observability.

Build the future of AI, one system at a time

Go beyond prompting.

Learn how modern AI systems are designed, trained, fine-tuned, retrieved, orchestrated, evaluated, secured and deployed, with lead mentor Mian Ahmad Basit.

Get 10% off the Master Cohort

Enroll before the timer ends and mention the code in your application. Code: MASTER10

Explore the curriculum

Python
ML
DL
NLP
Transformers
LLMs
Fine-Tuning
RAG
Agentic AI
Multi-Agent
AgentOps
Cloud
Production

Offer valid until the timer ends. Seats are limited.

Production LLM & Agentic AIEngineering Master Program

Your learning path

Mian Ahmad Basit

More from the faculty

Zeeshan Ali

Ahmad Hasan

20 learning phases

900+ topics and subtopics

Production-focused

AI + ML + DL + GenAI

AWS, GCP, Azure and VPS

Hands-on projects

What is included

Programming & AI Foundations

Skills you gain

Tools you use

Modules in this phase

Download the full master syllabus (PDF)

Programming & AI Foundations

Machine Learning Foundations

Deep Learning Foundations

NLP Foundations

AI Foundations & Transformers 101

Prompts to Programs & Programmatic LLMs

KV Cache & Advanced Attentions

Finetuning Language Models & PEFT

Mixture of Experts & Reasoning Models

Knowledge Distillation & Small Language Models

Multimodal Vision Systems & Image Understanding

Speech AI & Conversational Audio Systems

Representation Learning & Embedding Systems

Production RAG Systems

Agentic Systems: Core Architectures & Foundations

Agents: Orchestration, Protocols & Production

Context Engineering & Harness Engineering

LLMOps & Observability

Production Infrastructure & Cloud Deployment

AI Security, Guardrails & RBAC

Direct mentorship, guaranteed

n8n

Claude

OpenAI ChatGPT

NVIDIA NeMo & NIM

Hugging Face

DeepSeek-R1 & V3

Meta LLaMA 3.3

Google Gemini

LangGraph & LangChain

Qdrant & Pinecone

vLLM & Ollama

FastMCP & Docker/K8s

Models, providers and automation

Frontier models & providers

Workflow & agent automation

01NeMo security gate

02LangGraph supervisor

03FastMCP & A2A tools

04vLLM & Inspect AI

Want to build these architectures from scratch?

NVIDIA NeMo Guardrails

Microsoft Presidio PII masking

OWASP Top 10 for LLMs defense

Private air-gapped VPC deployment

The security promise

Agent Frameworks

LangChain

LangGraph

OpenAI Agents SDK

Google ADK

AWS Strands

CrewAI

LlamaIndex

n8n

LangFlow

Coding Agents & IDEs

Claude Code

OpenClaw

NemoClaw

Hermes Agent

Codex CLI