Enrollment Is Now Open
An advanced certification designed to prepare you for senior, high-impact LLM engineering roles. Learn the skills tech employers hire for — from fine-tuning and evaluation to deploying optimized models with open-source tools.
Production-ready skills for fine-tuning, optimizing and deploying LLMs
Watch real instructional videos from the LLM Engg. and Deployment Program
From Unit 2 of the program, this video explains how floating-point data types affect memory, precision, and performance. You'll see how FP32, FP16, and BF16 behave in practice.
These concepts are foundational for understanding LoRA, QLoRA, and modern parameter-efficient fine-tuning workflows.
Learn alongside hundreds of AI engineers building real-world LLM systems
USD $149 early access pricing available until January 15, 2026 (Regular price USD $299)
Payable in your local currency at checkout.
Credentials that demonstrate real-world LLM training and deployment expertise
This certification program was developed after analyzing hundreds of job postings for AI and LLM engineering roles. We identified the most in-demand skills - from fine-tuning with Hugging Face and LoRA to real-world deployment and evaluation techniques - and built a practical, project-based curriculum to match. Whether you're upskilling or transitioning roles, you'll learn what teams are hiring for today.
Build portfolio-grade projects, earn a professional certificate, and stand out to employers
USD $149 early access pricing available until January 15, 2026 (Regular price USD $299)
Payable in your local currency at checkout.
This is a self-paced, project-based certification focused on real-world LLM engineering workflows.
You'll apply what you learn by building and shipping two practical projects that mirror how LLMs are developed and deployed in industry.
The goal is simple: gain confidence working with large language models by fine-tuning, evaluating, and deploying them yourself.
You'll get full access to all lessons, walkthrough videos, and example code.
The program includes two milestone projects: one focused on fine-tuning and optimization, and one focused on deployment and inference.
To earn the LLM Engineering & Deployment Certificate, you must:
If needed, you can revise and resubmit — just like in a real engineering workflow.
A two-module journey covering the full LLM engineering lifecycle
Industry-grade LLM fine-tuning projects built as part of the program
Qwen2.5-1.5B Fine-tuning Project
Efficient Recipe Generator
A domain-adapted language model fine-tuned with QLoRA to generate high-quality recipes.
Qwen2.5-1.5B-Instruct Fine-Tuning
Text-to-SQL Generator
A Qwen-based language model fine-tuned with QLoRA to generate accurate SQL from natural language.
Qwen 2.5 0.5B Instruct Fine-Tuning
Nigerian News Headline Generation
A lightweight language model fine-tuned with QLoRA to generate culturally relevant Nigerian news headlines.
QLoRA Fine-Tuning Project
Article Summarization
A Qwen-based language model fine-tuned with QLoRA to generate concise summaries of scientific articles.
Build your own with the LLM Engineering and Deployment Program
USD $149 early access pricing available until January 15, 2026 (Regular price USD $299)
Payable in your local currency at checkout.
Learn from an Industry Leader Working at the Cutting Edge of Generative AI
This program isn't theory-first or trend-driven. It's built from real hiring data and hands-on experience fine-tuning and deploying LLMs in production. My goal was to create the kind of training I wish I had when starting out — practical, focused, and career-relevant.Abhyuday Desai, Ph.D.Founder & CEO of Ready Tensor, Lead Instructor for this Program
Answers to common questions about the LLM Engineering & Deployment Certification Program.
This program is designed software developers, AI/ML engineers, applied researchers, NLP developers, MLOps practitioners, and data scientists who want hands-on experience with fine-tuning, evaluation, and enterprise-grade deployment of large language models.
This program assumes prior experience with Python, PyTorch, and core machine learning workflows. If you're new to neural network training, especially using PyTorch, we recommend completing a foundational program to build the necessary background before enrolling.
You should be comfortable with intermediate Python programming, familiar with Hugging Face Transformers package and PyTorch, and have experience using LLM APIs.
Most learners complete the program in 5-8 weeks. The exact timeline depends on your prior experience and how much time you dedicate each week.
The program consists of 10 units. Each unit typically takes between 5-10 hours to complete, depending on the topic and your prior experience. Some units are lighter and more conceptual, while others are more hands-on and time-intensive.
The program is fully self-paced. Project submissions are reviewed on a regular schedule, but you can submit your work whenever you're ready.
Yes. You'll need access to cloud GPU resources and, in some cases, API keys. The program uses platforms like RunPod and AWS. Most learners spend approximately $10-$20 on cloud resources, and costs may be lower if you have access to local GPUs.
You'll receive detailed feedback from the reviewers and can revise and resubmit your project. Iteration and improvement are treated as part of the learning process.
Completing the full program earns you the Certified LLM Engineer certificate and a shareable digital badge. You can also earn module-level certificates for LLM Fine-Tuning Specialist and LLM Deployment Engineer by completing the respective projects.
This program prepares you for LLM engineering roles focused on model fine-tuning, evaluation, inference optimization, and deployment. These skills are commonly required for senior AI Engineer, Machine Learning Engineer, and applied LLM engineering roles.
Join hundreds of AI engineers worldwide developing and deploying production-grade LLM systems
USD $149 early access pricing available until January 15, 2026 (regular price USD $299)
Payable in your local currency at checkout.