AI Engineering Masterclass
Build 8 LLM applications and master the tech stacks behind LLMs
By the end of each module, you'll build & deploy an LLM application:
What You'll Master
This curriculum provides a deep dive into the essential techniques powering the end-to-end LLM lifecycle.
The Production-Grade Stacks throughout LLM Lifecycle
You will learn to navigate production-grade stacks, selecting the right tools and architectures tailored to your specific project needs.
By the end of the course, you will be able to implement these techniques and deploy LLM systems into production environments.
For Developers, Data Scientists, Builders
This course is designed for those who want to bridge the gap between AI Enthusiast and AI Engineer.
- Software Developers: Learn how to integrate LLMs into your existing tech stack without the fluff.
- Data Scientists: Move from static notebooks to functional, agentic applications.
- Self-Taught Builders: Get the diagrams and source code you need to turn your ideas into a portfolio-ready MVP.
The Full Curriculum
From LLM Foundations to Multi-Modal Agents
Module 1
The LLM Backbone: Building a RAG-Based Custom GPT from Scratch
Explore the core mechanism and hands-on implementation of RAG, tokenizer, and inference logic.
pytorchtensorhuggingfacetransformerswarcstreamlituvYou'll Build: Web Summarizer Custom GPT
Production Goals:
Implement custom BPE tokenizer, logits adjustment, and major decoding methods.
What You'll Master:
- Perform Common Crawl & Heuristic filtering.
- Build a BPE tokenizer to map text to tokens.
- Adjust logits via logits bias, temperature, and repetition penalty.
- Interactively apply stochastic/deterministic decoding methods.
- Deploy the inference via an API as a microservice.
Module 2
CodeContext: Low-Latency Neural Code Search Engine
A production-grade retrieval system designed to navigate complex repositories using ColBERT for token-level semantic matching and Redis for sub-10ms cache hits.
ragatouilleredisfastapipython-astdockeropenllmetryYou'll Build: Neural Code Intelligence Engine
Production Goals:
Minimize retrieval latency to sub-100ms for real-time developer workflows.
Achieve 100% recall on specific code patterns (e.g., middleware logic, retry policies).
What You'll Master:
- Late Interaction (ColBERT) for fine-grained, token-sensitive code search.
- Semantic Caching (Redis) to accelerate recurrent architectural queries.
- Custom AST Parsing for structural code chunking and function-level indexing.
- Observability & Tracing via OpenLLMetry to profile retrieval pipeline bottlenecks.
Module 3
Digital Clone: Persona Fine-Tuning & Edge Distillation
Engineered a high-fidelity interactive persona by distilling linguistic patterns from frontier models into a localized 3B parameter footprint.
unslothtrltransformersggufvllmsagemakerboto3openaiYou'll Build: Edge-Native Digital Clone (Smartphone/Web)
Production Goals:
Compress GPT 5.4 mini intelligence for edge AI.
What You'll Master:
- Distill latent reasoning and Chain-of-Thought (CoT) capabilities from GPT-5.4 into a 3B model.
- Engineer multi-stage tuning pipeline - SFT for grounding, RKD for logic, and DPO for stylistic parity.
- Standardize input/output schemas using chat templates.
- Implement 4-bit quantization (GGUF) to balance VRAM efficiency and perplexity for edge hardware.
- Deploy via AWS SageMaker LMI/vLLM engine for paged-attention concurrency and real-time streaming.
Module 4
The Revenue Engine: Self-Optimizing Ad-Creative Agent
Build an autonomous system that generates, audits, and self-corrects ad creatives to maximize Click-Through Rate (CTR).
LangGraphFastAPIRedisGPT-4o/Llama-3PostgreSQLYou'll Build: A Self-Learning Digital Marketing Suite
Production Goals:
Connect Generative AI directly to business revenue metrics (ROI).
Implement a self-correcting data flywheel for automated creative iteration.
Achieve inference-time scaling via multi-agent adversarial debate.
What You'll Master:
- Multi-Agent Orchestration with LangGraph
- Closed-loop feedback for continuous model improvement
- Inference-time Scaling (System-2 Thinking)
- Synthetic Reward Signals for CTR Simulation
- Bayesian A/B Testing Simulator for prompt refinement
Module 5
Autonomous Executive Agent
Master the Action layer. Build a fault-tolerant agent that executes multi-step tasks like bookings and scheduling.
LangGraphPEFTTavily APIPostgreSQLFastAPIYou'll Build: A Hotel Booking Assistant
Production Goals:
Implementing persistent agency that survives server crashes.
Managing high-stakes tool calling with human-in-the-loop.
What You'll Master:
- QLoRA fine-tuning for high-precision tool calling.
- Parallel tool execution for complex multi-step tasks.
- Adapter-based training for JSON reliability.
- Interrupt-driven workflows (Human-in-the-loop).
- Fault-tolerant retry logic for API failures.
Module 6
System-2 Thinking Assistant: Multi-Step Reasoning Engine
A high-fidelity reasoning partner that implements planning, adversarial debate, and self-critique to solve complex problems where standard sequential LLM output fails.
langgraphdeepseek-r1openrouterpydantic-aipythonYou'll Build: Decision Reasoning & Planning Assistant
Production Goals:
Scale inference-time compute to improve decision quality on high-stakes tasks.
Implement a transparent, verifiable 'Chain-of-Thought' visualization for user trust.
What You'll Master:
- Tree-of-Thoughts (ToT) for exploring multiple reasoning branches.
- Adversarial Multi-Agent Debate to reach consensus through conflict.
- Self-Critique & Reflection loops for iterative logic refinement.
- Sequential Planning-Execution-Verification (PEV) cycles.
- Inference-time compute scaling inspired by DeepSeek-R1 logic.
Module 7
The Smart Fridge: Visual Grounding & Multi-modal RAG
Identify ingredients from photos and generate real-time meal plans using multi-modal retrieval logic.
LlavaTensorRTPEFTChromaDBYou'll Build: A Smart Fridge App
Production Goals:
Accurate object detection and inventory matching in unconstrained environments.
What You'll Master:
- Fine-tuning multi-modal projectors with Vision-LoRA
- Multi-modal embeddings for image similarity search
- Optimizing multi-modal inference with TensorRT-LLM
- CLIP score evaluation for image-text alignment
- Video latent caching for inventory tracking
Module 8
The AI Architect: Production Governance & Fiscal Control
Bridge the gap between functionality and security. Build a centralized governance layer to automate red-teaming, enforce compliance, and audit every token for maximum ROI.
LlamaGuardNeMo GuardrailsWeights & BiasesAWS/GCPYou'll Build: Enterprise LLM Guardrails & Cost-Per-Query Dashboard
Production Goals:
Transit from prototypes to enterprise systems by integrating real-time guardrails.
Establish a programmatic evaluation (LLM-as-a-Judge) pipeline for rigorous regression testing.
Maximize unit-economics through real-time token ROI modeling.
What You'll Master:
- Programmatic evaluation (LLM-as-a-Judge) for regression testing
- Automated PII redaction and data masking
- Token ROI modeling for cost-to-value analysis
- Prompt versioning with Git-based prompt management
- Red-teaming simulation over prompt injection and jailbreak attacks
✅ Bespoke Implementation
The Practical AI Engineer curriculum provides the full architectural blueprint to build and own your AI stack.
For those looking to bypass the setup phase and go straight to production, I provide high-touch engineering support to integrate these systems into your unique business environment.
Accelerate Your Deployment with Expert Engineering
If you require a direct path to a live MVP, I offer specialized boutique services:
1. 14-Day AI Implementation Sprint
I'll deploy a custom RAG or Agentic workflow (based on the course architectures) into your private cloud environment, tailored to your proprietary data.
2. Data Pipeline Engineering
Transform raw, unstructured data into high-fidelity assets for your LLMs.
3. Reliability & Security Audit
Audit cost, latency, reliability, and security of your AI pipelines.