Learn and build your own LLM
Dive deep into the architecture of Large Language Models. From understanding transformers to training a custom miniature LLM from scratch.
Full access to curriculum, live sessions, systems architecture guidance, and private cohort network.
Dedicated A100 GPU Sandbox for training runs and 25+ hours of implementation-focused labs.
Secure checkout via Stripe / Global Cards
About this program
This program is not about prompt engineering. It's for engineers who want to understand the exact mathematical and architectural foundations of modern AI. You will build a transformer from the ground up in PyTorch, write your own tokenizer, curate a dataset, and execute a full training loop on a GPU.
Who is this for?
Backend Engineers, Data Scientists, AI Researchers
What you'll actively build & learn
Understanding Fundamentals
Grasp the core mechanics of AI systems, from transformers to retrieval algorithms, moving beyond superficial APIs.
Production-Ready Architecture
Learn how to architect scalable, resilient generative AI applications that handle edge cases and high throughput.
Hands-on Engineering
Write custom PyTorch models, build multi-agent swarms using LangGraph, and deploy to Kubernetes.
Verifiable Execution
Complete rigorous capstone projects that serve as a proof-of-work portfolio for your next AI engineering role.
Time Commitment & Schedule
Live Engineering
2-3 hrs / week
Deep-dive interactive technical sessions focusing on architecture, code walkthroughs, and edge cases. Fully recorded.
Independent Build
4-6 hrs / week
Asynchronous reading materials, implementing weekly milestones, and collaborating via Discord for unblocking code errors.
Weekly Syllabus
Each week is structured around three things: what you'll cover, what capability you'll walk away with, and the concrete deliverable that moves you toward the final capstone.
8 weeks with guided build milestones
A trained and presented miniature LLM system
Weekly implementation-focused labs plus capstone reviews
The Mathematics of Attention
- We begin by dissecting the core mathematical operations behind Scaled Dot-Product Attention and Multi-Head Attention.
- You will manually implement these mechanisms in PyTorch, avoiding high-level abstractions, to deeply understand tensor broadcasting, masking strategies, and how Rotary Positional Embeddings (RoPE) maintain context aware sequences.
Understand and implement attention mechanics from first principles.
A working attention notebook with tensor walkthroughs.
Architecting the Transformer Block
- Moving beyond isolated layers, we assemble the full Decoder-only transformer architecture.
- We'll implement Feed-Forward Networks (FFNs) with SwiGLU activations, RMSNorm for training stability, and residual connections.
- By the end of this week, you will have a functional, untrained model capable of forward passing dummy tensors.
Assemble the core transformer stack and validate forward passes.
A modular decoder-only transformer implementation.
Tokenization & Data Engineering
- Models are only as good as their data.
- You will build a Byte-Pair Encoding (BPE) tokenizer from scratch, understanding vocabulary compression and out-of-vocabulary (OOV) handling.
- We then write custom PyTorch DataLoaders to efficiently stream and pre-process gigabytes of text data without blowing up system memory.
Prepare a training-ready text pipeline with tokenizer and loaders.
A tokenizer build and data pipeline for model training.
The Training Loop & Optimization
- We dive headfirst into backpropagation and optimization.
- You'll construct the training loop, configure AdamW optimizers with weight decay, implement Cosine Annealing learning rate schedulers with warmup, and handle vanishing/exploding gradients.
- We also introduce Mixed Precision Training (FP16/BF16) to drastically accelerate computation.
Train the model with a stable optimization loop.
A reproducible training run with metrics and checkpoints.
Scaling Up: Distributed Training
- A single GPU isn't enough for modern foundational models.
- This week focuses entirely on parallelization strategies.
- We will cover Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP), teaching you how to orchestrate multi-GPU sweeps and handle cross-node communication bottlenecks effectively.
Understand how training scales beyond a single-device setup.
A distributed training experiment plan and working setup.
Fine-Tuning & Local Adaptation
- Pre-training teaches the model language; fine-tuning teaches it behavior.
- We will explore Instruction Tuning pipelines and implement Parameter-Efficient Fine-Tuning (PEFT) methods—specifically LoRA and QLoRA.
- You'll learn how to inject low-rank adapters into your base model to teach it specific dialects or tasks cheaply.
Adapt a base model to a narrower behavior or instruction set.
A fine-tuned checkpoint using a PEFT workflow.
Inference Optimization & Serving
- Generating text naively is incredibly slow.
- We address production inference bottlenecks by implementing KV Caching to prevent redundant computations and integrating Flash Attention for memory-efficient processing.
- Finally, we package the model for serving using vLLM to achieve high-throughput concurrency.
Prepare the model for practical serving and faster inference.
A deployable serving setup with optimized inference path.
Capstone: Model Presentation
- The ultimate test of your execution.
- You will demo your custom-trained, end-to-end LLM to the cohort and industry guests.
- You'll discuss your specific architectural choices, data curation hurdles, loss curves over time, and demonstrate your model running inference live on specific prompt tasks.
Present a complete end-to-end LLM system and defend the design choices.
A capstone demo, presentation, and model evaluation summary.
The syllabus builds toward a final proof of work.
The weekly syllabus is designed to stack toward a capstone that demonstrates what you can actually build. By the end of the cohort, you are not just finishing modules. You are presenting a concrete output that ties the learning arc together.
View Alumni CapstonesIndustry-Grade Certification
Earn a credential that actually matters. Every certificate is tied to your Capstone Project repo, valid for life, and optimized for your professional technical profile.
View Certification TiersEngineering Trust
Our alumni don't just 'use' AI. They architect the core infrastructure at forward-thinking engineering labs. This is a high-trust collective of senior talent.
"We've created a zero-noise environment for senior talent. This is where staff and principal engineers from Silicon Valley and beyond come to cross-pollinate their knowledge of agentic systems and distributed training."
The most technically rigorous program I've attended. No fluff, just pure architectural deep-dives into transformer blocks and swarm logic. This isn't just about calling APIs; it's about understanding the stochastic internals of LLMs.
LangGraph and Multi-agent orchestration was the missing link for our production pipeline. Highly recommended for senior devs who need to move beyond single-prompt engineering into complex, stateful workflows.
Direct 1:1 access to instructors who are actually shipping AI products. The focus on evaluations and evals-driven-dev is unique. We've implemented their RAG evaluation pipeline for our entire stealth startup.
Lead Instructor
Deep pedagogical philosophy balanced with production engineering rigor.
Meet
Anubhav
Anubhav is an AI solutions and engineering leader with two decades of global experience executing machine learning, generative AI, and physical intelligence initiatives.
With a proven track record of founding startups and building 0-to-1 engineering teams, he has architected and delivered production-grade systems across B2B SaaS, industrial robotics, sports tech, and massive-scale consumer streaming platforms serving over 600 million users.
At skilling academy, he personally mentors every student, bringing extensive experience in enterprise strategy, multi-agent workflows, computer vision, and scalable distributed architectures from the boardroom to the IDE.
Technical Expertise
- Transformers / Attention
- GNNs & Graph Search
- RLHF / DPO Alignment
- Distributed Training
- vLLM / NVIDIA Triton
- Kubernetes / Ray
- VectorDB Scaling
- Hybrid Retrieval
- Knowledge Graphs
- Autonomous Execution
- ReAct / Tool-use
- Planner Architectures
System FAQ
Addressing technical edge cases and curriculum logistics for the committed engineer.
Our cohorts are crafted for mid-to-senior level software engineers, data scientists, and technical product managers who are comfortable with Python and basic web architecture. If you've been 'prompt engineering' but want to understand the underlying mechanics—transformer blocks, vector algebra, and autonomous agent orchestration—this is for you.
Plan for 6-8 hours of focused effort per week. This breaks down into 2 hours of live, interactive deep-dives on Saturdays, 1 hour of midweek Q&A/Office Hours, and 3-5 hours of dedicated hands-on project implementation where you'll build production-ready AI modules.
Life happens. Every live session is recorded in 4K and uploaded to our private portal within 2 hours. You'll have lifetime access to these recordings, including all updated versions of the curriculum. Our Discord community and mentors are active 24/7 to help you get back on track.
Not necessarily. While we discuss hardware optimization, most of our practical work utilizes cloud-based environments (Google Colab, Modal, or Lambda Labs). We provide credits and setup guides so you can run large-scale inference and fine-tuning without burning through your own hardware.
We keep cohorts focused (max 60) to maintain a high mentor-to-student ratio. You’ll be split into smaller review pods, and you’ll get dedicated feedback via office hours and code review workflows. This keeps discussions high-bandwidth and practical.
We teach 'First Principles'. While we use popular frameworks for speed, we spend significant time building core components (like Custom RAG retrievers or ReAct loops) from scratch. This ensures that when the next big framework arrives, you'll understand exactly how it works under the hood.
Absolutely. Our final project is a portfolio-grade AI system that solves a real business problem. We also provide a dedicated session on the AI Engineering interview landscape, resume reviews for technical roles, and introductions to our network of hiring partners in the AI space.
We want you to be 100% satisfied. If after the first week you feel the cohort isn't the right fit, we offer a full, no-questions-asked refund. Our goal is to build a community of committed builders, and we stand by the quality of our curriculum.
Yes. All students get lifetime access to our internal repository of production-ready templates, deployment scripts, and evaluation benchmarks. These are the same tools our instructors use to build and scale AI solutions in their day-to-day professional work.
Upon successful submission and review of your final 3 project modules, you will receive a cryptographically signed digital certificate. This certificate is recognized by our network of partner companies and can be directly shared on LinkedIn or included in your professional portfolio.