Get 10% off the Master Cohort· ends in
Lead instructor & CTO: Mian Ahmad Basit
Stop building toy demos. Start shipping production AI systems.
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.
Live weekend and weekdays sessions with direct mentor guidance
Technical educators and system architects guiding you from Python basics to autonomous multi-agent systems.
CEO, CTO & Lead AI Engineering Trainer
AI Engineer - GenAI - Agentic AI - Cloud
One structured path from Python basics to secure, cloud-deployed agentic systems.
From Python and ML to advanced agentic AI
Complete technical depth, nothing skipped
Evals, guardrails, RAG and observability
Complete foundations to state of the art
Cloud orchestration and multi-cloud deployment
Real agentic architectures and systems
Pick a phase to see its goals, skills, tools and modules.
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
Phase 1 of 20
Every topic, protocol and deployment strategy, organised by phase. Search or filter to find exactly what you need.
Save every phase, module, topic and project outline to your device.
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
Functions & Functional Paradigms
Files, Exceptions & Environments
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
Advanced Python Mechanics
Async Programming & APIs
Key outcome
Engineer asynchronous, typed and modular Python services ready for AI workloads.
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
Probability & Statistics
Calculus & Optimization
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
ML Engineering & Evaluation
Key outcome
Train, evaluate and serialize classical ML models with production-grade pipelines.
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
Normalization & Regularization
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
CNNs & Sequence Models
Key outcome
Build and train CNNs and sequence models in PyTorch, ready for Transformer architectures.
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
Key outcome
Build classical NLP pipelines and understand the representations that lead directly into Transformers.
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
Attention Mechanics
Transformer Architectures
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
Subword Algorithms
Vocabulary Engineering
Key outcome
Build and analyze tokenizers while understanding exactly how text becomes model input tokens.
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
Reasoning Strategies
Prompt Reliability
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
Outlines Guided 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
DSPy Optimizers
Key outcome
Compile an optimized DSPy program instead of manually tuning prompts.
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
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
Attention Variants
Scaling Laws
Key outcome
Analyze and optimize LLM attention memory, throughput and compute requirements for production inference.
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
Data Preparation
Synthetic Data
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
Supervised 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
Quantization & Inference Preparation
Training Tooling
Synthetic Preference Data
Key outcome
Align, quantize and prepare an open-weight LLM for efficient production inference.
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
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
Key outcome
Explain how reinforcement learning produces modern reasoning-capable 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
Key outcome
Design and deploy compact, edge-ready small language models.
Compress foundation-model capabilities into smaller student models.
Knowledge Distillation
Key outcome
Build a teacher-student distillation pipeline that compresses a large model into a compact one.
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
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
Key outcome
Explain and build the encoder-projector-LLM pipeline behind modern VLMs.
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
Key outcome
Fine-tune Whisper on domain-specific audio and build streaming speech-to-text 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
Matryoshka Representation Learning
Embedding Fine-Tuning & Evaluation
Key outcome
Select, fine-tune, compress and benchmark embeddings for enterprise retrieval workloads.
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
LlamaIndex Ecosystem
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
Advanced Retrieval
Self-Reflective RAG
Vector Quantization
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
Graph RAG & Vectorless RAG
Caching & Security
Key outcome
Ship a secure multimodal and graph-enhanced enterprise RAG system with caching and access controls.
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
LangGraph Architecture
Agent State & Memory
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
Agent Tools & Validation
Streaming & Multi-Agent Systems
Evals & Observability
Key outcome
Build type-safe, testable agents with automated evaluation and production-grade observability.
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
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
Function Calling
Agent Observability
Key outcome
Connect multiple agents through A2A, execute robust function calls and trace multi-agent workflows end to end.
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
Memory Architectures
Context Compression
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
Secure Execution
ReAct Safety Harnesses
Key outcome
Evaluate agents automatically and execute untrusted agent-generated code inside controlled sandboxes.
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
Caching & Cost Control
Key outcome
Instrument production AI workloads with end-to-end telemetry, evaluation and automated cost controls.
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
Cloud Infrastructure
Containerization & CI/CD
Key outcome
Deploy a scalable LLM inference platform on Kubernetes with automated container builds and CI/CD.
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
LLM Gateways & Routing
Identity, RBAC & Enterprise Security
Security Evaluation
Key outcome
Deploy enterprise AI behind guardrails, secure gateways, PII protection and tenant-aware RBAC controls.
Lead instructor, CTO @ PBI & Anz Tech
“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
No toy libraries. You get hands-on practice with n8n, Claude, OpenAI, NVIDIA NeMo, Hugging Face, DeepSeek and Meta LLaMA.
Workflow automation
Self-hosted AI
Enterprise AI agent workflow orchestration, webhooks and FastMCP nodes
Anthropic AI
Computer Use & FastMCP
Artifacts, Computer Use, prompt caching and high-reasoning agent execution
Frontier LLM
Realtime & function calling
Structured JSON outputs, Assistants API, voice realtime and o1/o3 reasoning
Enterprise security & acceleration
NeMo Guardrails & NIM
Prompt injection defense, PII masking, TensorRT-LLM microservices
Open-source ecosystem
Transformers & TGI
Model Hub, datasets, Text Generation Inference, PEFT and safetensors
Reasoning & MoE
Chain-of-thought (CoT)
Multi-head Latent Attention (MLA), GRPO rule-based RL and self-hosted distillation
Open weights
8B / 70B / 405B
QLoRA 4-bit, DPO alignment, Unsloth acceleration and on-prem deployment
Multimodal frontier
2M token context
Context caching, ColPali vision RAG and video document processing
Agent framework
Stateful swarms
Human-in-the-loop interrupts, state checkpointing and A2A routing
Vector database
HNSW & ColBERT
High-density vector indexing, hybrid BM25 retrieval and payload filtering
LLMOps serving
PagedAttention
Continuous batching, LoRA multi-serving and local on-prem cluster execution
Protocol & cloud infrastructure
Helm & Kubernetes
Model Context Protocol, containerized microservices and auto-scaling pods
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
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
Run the live trace to watch guardrails, tool calls and multi-agent protocols working together.
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
PII masking with Presidio, prompt injection defense and input policy validation.
Stateful graph routing, checkpoint persistence and human-in-the-loop interrupts.
Standardized Model Context Protocol servers and inter-agent delegation.
High-throughput model serving with LoRA adapter routing and eval-gated score checks.
Get the full syllabus and join the Master Cohort.
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
Prevent hallucination, enforce strict brand dialog boundaries and block prompt injection attempts in real time.
Data redaction and anonymization
Automatically strip social security numbers, credit cards, emails and sensitive credentials before they reach LLM APIs.
Jailbreak and attack hardening
Protect against indirect prompt injection, vector database denial-of-service, data poisoning and unauthorized tool calls.
On-prem DeepSeek & LLaMA
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)
“I teach the exact zero-leakage security patterns required by banks, health networks and Fortune 500 enterprises.”
From agent orchestrators and coding assistants to protocols, memory systems, observability and cloud environments.
Ecosystem for LLM application components and LCEL pipelines.
Stateful, cyclic multi-agent graph orchestration.
Official OpenAI SDK for agent loops and tool execution.
Google Agent Development Kit for Gemini and Vertex AI.
AWS framework for building cloud-native multi-agent swarms.
Role-based multi-agent orchestration framework.
Data framework for LLMs, RAG and query workflows.
Visual node-based workflow automation for AI tools.
Interactive visual UI for building and testing AI agents.
Anthropic agentic CLI for workspace code refactoring.
Open-source autonomous coding agent.
NVIDIA NeMo backed coding assistant.
Autonomous agent CLI built for persistent terminal execution.
Command-line interface for OpenAI code generation.
Open terminal coding agent for automated repositories.
Agentic AI coding workspace powered by Gemini.
Open standard for connecting AI models to tools and data sources.
Standardized protocol for inter-agent task delegation and discovery.
Universal long-term memory layer for AI agents and assistants.
State persistence and time-travel debugging in graph agents.
Contextual memory modules for conversational query engines.
Knowledge graph powered retrieval augmented generation.
Agent benchmarking, cost analysis and execution tracking.
Full-suite tracing, debugging and testing platform.
Open-source LLM evaluation and monitoring engine.
Open-source observability, prompt management and analytics.
Span-level telemetry from the Pydantic team.
Retrieval Augmented Generation for external knowledge grounding.
Agent-driven dynamic search, self-correction and synthesis.
Optimizing context windows, pruning and memory layout.
CoT, few-shot and structured prompt optimization.
Data validation and typed schemas for LLM contracts.
High-speed vector similarity indices.
Task, solver and scorer evaluation harnesses.
Multi-tenant isolation, prompt defense and JWT RBAC.
Input/output scanners, toxicity filters and PII masking.
Facebook AI Similarity Search vector library.
Search API optimized for LLM agent research loops.
Multi-stage containerization for reproducible builds.
Automated CI/CD workflows for testing and deployment.
High-performance Python web framework for async AI APIs.
Unified framework for serving and packaging ML models.
High-performance reverse proxy and SSL termination.
Rapid Python web UI prototyping for data and AI.
Interactive Python interfaces for machine learning models.
Amazon Web Services cloud suite for AI workloads.
Google Cloud Platform AI and serverless containers.
Microsoft Azure enterprise cloud platform.
Bare-metal and private virtual servers for cost efficiency.
Frontier models and structured output endpoints.
High intelligence reasoning and coding models.
Multimodal 2M+ token context window models.
Ultra-fast LPU inference engine for open-source LLMs.
Local LLM runner for LLaMA-3, DeepSeek-R1 and Phi-3.
Managed foundation models on Amazon Web Services.
Enterprise-grade managed OpenAI endpoints on Azure.
AI engineering is one of the most in-demand technical skill sets. These ranges show what skilled engineers can charge.
Full-time jobs
$120K - $250K / yr
Top remote positions across US, UK, EU and UAE companies seeking specialized agentic and RAG engineers.
Upwork & LinkedIn
$80 - $250 / hr
High-ticket hourly rates for custom DeepSeek fine-tuning, vector database optimization and agent setups.
Per-project contracts
$5,000 - $25,000
Building multi-tenant AI SaaS, self-hosted n8n automation and confidential enterprise RAG pipelines.
Estimate your monthly revenue from part-time remote consulting.
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.
Beyond the technical curriculum, enrollment includes career placement support, freelancing frameworks, LMS access and direct mentorship.
Hands-on practical projects
Zero fluff or toy demos. You build production-grade, containerized LLM and agent systems on AWS, GCP and Kubernetes.
Included in the program
LMS recorded lectures
Never miss a single insight. Revisit HD class recordings, code repositories and slide decks anytime for 24 months.
Included in the program
Internship opportunities
Fast-track your career with internship referrals at partner organizations, Pakistan Blockchain Institute and Anz Technologies.
Included in the program
Freelancing & business development
Learn how to monetize your skills, win high-ticket $5,000-$25,000 AI consulting clients and launch scalable AI SaaS products.
Included in the program
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.”
Answer 3 quick questions to find your personalized entry point.
Question 1 of 3
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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
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
Offer valid until the timer ends. Seats are limited.
